diff --git a/codecov.yml b/codecov.yml
index c72cca6b3d..17ff4479e6 100644
--- a/codecov.yml
+++ b/codecov.yml
@@ -5,7 +5,6 @@ coverage:
         target: 70%
         threshold: 10
         base: parent
-        branches: null
         if_no_uploads: error
         if_not_found: success
         if_ci_failed: error
@@ -18,7 +17,6 @@ coverage:
         # Allows PRs without tests, overall stats count
         threshold: 100
         base: auto
-        branches: null
         if_no_uploads: error
         if_not_found: success
         if_ci_failed: error
diff --git a/examples/README.md b/examples/README.md
index 61c3d7ecf9..21065dd67b 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -10,17 +10,17 @@ python -m pip install -U notebook
 ```
 
 ### 2. List of examples
-#### 1. [classification_3d](https://github.com/Project-MONAI/MONAI/tree/master/examples/classification_3d)
+#### [classification_3d](./classification_3d)
 Training and evaluation examples of 3D classification based on DenseNet3D and [IXI dataset](https://brain-development.org/ixi-dataset):
 The examples are standard PyTorch programs and have both dictionary-based and array-based transformation versions.
-#### 2. [classification_3d_ignite](https://github.com/Project-MONAI/MONAI/tree/master/examples/classification_3d_ignite)
+#### [classification_3d_ignite](./classification_3d_ignite)
 Training and evaluation examples of 3D classification based on DenseNet3D and [IXI dataset](https://brain-development.org/ixi-dataset):
 The examples are PyTorch ignite programs and have both dictionary-based and array-based transformation versions.
-#### 3. [notebooks/multi_gpu_test](https://github.com/Project-MONAI/MONAI/blob/master/examples/notebooks/multi_gpu_test.ipynb)
+#### [notebooks/multi_gpu_test](./notebooks/multi_gpu_test.ipynb)
 This notebook is a quick demo for devices, run the Ignite trainer engine on CPU, GPU and multiple GPUs.
-#### 4. [notebooks/nifti_read_example](https://github.com/Project-MONAI/MONAI/blob/master/examples/notebooks/nifti_read_example.ipynb)
+#### [notebooks/nifti_read_example](./notebooks/nifti_read_example.ipynb)
 Illustrate reading NIfTI files and iterating over image patches of the volumes loaded from them.
-#### 5. [notebooks/spleen_segmentation_3d](https://github.com/Project-MONAI/MONAI/blob/master/examples/notebooks/spleen_segmentation_3d.ipynb)
+#### [notebooks/spleen_segmentation_3d](./notebooks/spleen_segmentation_3d.ipynb)
 This notebook is an end-to-end training and evaluation example of 3D segmentation based on [MSD Spleen dataset](http://medicaldecathlon.com):
 The example shows the flexibility of MONAI modules in a PyTorch-based program:
 - Transforms for dictionary-based training data structure.
@@ -30,17 +30,20 @@ The example shows the flexibility of MONAI modules in a PyTorch-based program:
 - 3D UNet, Dice loss function, Mean Dice metric for 3D segmentation task.
 - Sliding window inference.
 - Deterministic training for reproducibility.
-#### 6. [notebooks/transform_speed](https://github.com/Project-MONAI/MONAI/blob/master/examples/notebooks/transform_speed.ipynb)
+#### [notebooks/transform_speed](./notebooks/transform_speed.ipynb)
 Illustrate reading NIfTI files and test speed of different transforms on different devices.
-#### 7. [notebooks/transforms_demo_2d](https://github.com/Project-MONAI/MONAI/blob/master/examples/notebooks/transforms_demo_2d.ipynb)
-This notebook demonstrates the medical domain specific transforms on 2D medical images.
-#### 8. [notebooks/unet_segmentation_3d_ignite](https://github.com/Project-MONAI/MONAI/blob/master/examples/notebooks/unet_segmentation_3d_ignite.ipynb)
+#### [notebooks/transforms_demo_2d](./notebooks/transforms_demo_2d.ipynb)
+This notebook demonstrates the image transformations on histology images using
+[the GlaS Contest dataset](https://warwick.ac.uk/fac/sci/dcs/research/tia/glascontest/download/).
+#### [notebooks/3d_image_transforms](./notebooks/3D_image_transforms.ipynb)
+This notebook demonstrates the transformations on volumetric images.
+#### [notebooks/unet_segmentation_3d_ignite](./notebooks/unet_segmentation_3d_ignite.ipynb)
 This notebook is an end-to-end training & evaluation example of 3D segmentation based on synthetic dataset.
 The example is a PyTorch Ignite program and shows several key features of MONAI,
 especially with medical domain specific transforms and event handlers.
-#### 9. [segmentation_3d](https://github.com/Project-MONAI/MONAI/tree/master/examples/segmentation_3d)
+#### [segmentation_3d](./examples/segmentation_3d)
 Training and evaluation examples of 3D segmentation based on UNet3D and synthetic dataset.
 The examples are standard PyTorch programs and have both dictionary-based and array-based versions.
-#### 10. [segmentation_3d_ignite](https://github.com/Project-MONAI/MONAI/tree/master/examples/segmentation_3d_ignite)
+#### [segmentation_3d_ignite](./examples/segmentation_3d_ignite)
 Training and evaluation examples of 3D segmentation based on UNet3D and synthetic dataset.
 The examples are PyTorch Ignite programs and have both dictionary-base and array-based transformations.
diff --git a/examples/notebooks/3D_image_transforms.ipynb b/examples/notebooks/3D_image_transforms.ipynb
new file mode 100644
index 0000000000..90324c5c7f
--- /dev/null
+++ b/examples/notebooks/3D_image_transforms.ipynb
@@ -0,0 +1,702 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Overview\n",
+    "This notebook introduces you MONAI's image transformation module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MONAI version: 0.0.1\n",
+      "Python version: 3.5.6 |Anaconda, Inc.| (default, Aug 26 2018, 16:30:03)  [GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]\n",
+      "Numpy version: 1.18.2\n",
+      "Pytorch version: 1.4.0\n",
+      "Ignite version: 0.3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Copyright 2020 MONAI Consortium\n",
+    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License.\n",
+    "\n",
+    "import sys\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# assumes the framework is found here, change as necessary\n",
+    "sys.path.append(\"../..\")\n",
+    "import monai\n",
+    "import monai.transforms.compose as transforms\n",
+    "from monai.transforms.composables import \\\n",
+    "    LoadNifti, LoadNiftid, AddChanneld, ScaleIntensityRanged, \\\n",
+    "    Rand3DElasticd, RandAffined, \\\n",
+    "    Spacingd, Orientationd\n",
+    "\n",
+    "monai.config.print_config()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data sources\n",
+    "Starting from a list of filenames. \n",
+    "\n",
+    "The following is a simple python script\n",
+    "to group pairs of image and label from `Task09_Spleen/imagesTr` and `Task09_Spleen/labelsTr`\n",
+    "folder. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_root = 'temp/Task09_Spleen'\n",
+    "\n",
+    "import os\n",
+    "import glob\n",
+    "train_images = sorted(glob.glob(os.path.join(data_root, 'imagesTr', '*.nii.gz')))\n",
+    "train_labels = sorted(glob.glob(os.path.join(data_root, 'labelsTr', '*.nii.gz')))\n",
+    "data_dicts = [{'image': image_name, 'label': label_name}\n",
+    "              for image_name, label_name in zip(train_images, train_labels)]\n",
+    "train_data_dicts, val_data_dicts = data_dicts[:-9], data_dicts[-9:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The image file names are organised into a list of dictionaries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'image': 'temp/Task09_Spleen/imagesTr/spleen_10.nii.gz',\n",
+       " 'label': 'temp/Task09_Spleen/labelsTr/spleen_10.nii.gz'}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_data_dicts[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The list of data dictionaries, `train_data_dicts`, could be used by\n",
+    "PyTorch's data loader.\n",
+    "\n",
+    "For example,\n",
+    "\n",
+    "```python\n",
+    "from torch.utils.data import DataLoader\n",
+    "\n",
+    "data_loader = DataLoader(train_data_dicts)\n",
+    "for training_sample in data_loader:\n",
+    "    # run the deep learning training with training_sample\n",
+    "```\n",
+    "\n",
+    "The rest of this tutorial presents a set of \"transforms\"\n",
+    "converting `train_data_dict` into data arrays that\n",
+    "will eventually be consumed by the deep learning models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the NIfTI files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One design choice of MONAI is that it provides not only the high-level workflow components,\n",
+    "but also relatively lower level APIs in their minimal functioning form.\n",
+    "\n",
+    "For example, a `LoadNifti` class is a simple callable wrapper of the underlying `Nibabel` image loader.\n",
+    "After constructing the loader with a few necessary system parameters,\n",
+    "calling the loader instance with a NIfTI filename will return the image data arrays, as well as the metadata -- such as affine information and voxel sizes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = LoadNifti(dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input: temp/Task09_Spleen/imagesTr/spleen_10.nii.gz\n",
+      "image shape (512, 512, 55)\n",
+      "image affine [[   0.97656202    0.            0.         -499.02319336]\n",
+      " [   0.            0.97656202    0.         -499.02319336]\n",
+      " [   0.            0.            5.            0.        ]\n",
+      " [   0.            0.            0.            1.        ]]\n",
+      "image pixdim [1.       0.976562 0.976562 5.       0.       0.       0.       0.      ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "image, metadata = loader(train_data_dicts[0]['image'])\n",
+    "print('input:', train_data_dicts[0]['image'])\n",
+    "print('image shape', image.shape)\n",
+    "print('image affine', metadata['affine'])\n",
+    "print('image pixdim', metadata['pixdim'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Oftentimes, we want to load a group of inputs as a training sample.\n",
+    "For example training a supervised image segmentation network requires a pair of image and label as a training sample.\n",
+    "\n",
+    "To ensure a group of inputs are beining preprocessed consistently,\n",
+    "MONAI also provides dictionary-based interfaces for the minimal functioning transforms.\n",
+    "\n",
+    "`LoadNiftid` is the corresponding dict-based version of `LoadNifti`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = LoadNiftid(keys=('image', 'label'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input: {'image': 'temp/Task09_Spleen/imagesTr/spleen_10.nii.gz', 'label': 'temp/Task09_Spleen/labelsTr/spleen_10.nii.gz'}\n",
+      "image shape (512, 512, 55)\n",
+      "label shape (512, 512, 55)\n",
+      "image pixdim [1.       0.976562 0.976562 5.       0.       0.       0.       0.      ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_dict = loader(train_data_dicts[0])\n",
+    "print('input:', train_data_dicts[0])\n",
+    "print('image shape', data_dict['image'].shape)\n",
+    "print('label shape', data_dict['label'].shape)\n",
+    "print('image pixdim', data_dict['image.pixdim'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "image, label = data_dict['image'], data_dict['label']\n",
+    "plt.figure('visualise', (8, 4))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.title(\"image\")\n",
+    "plt.imshow(image[:, :, 30], cmap='gray')\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.title(\"label\")\n",
+    "plt.imshow(label[:, :, 30])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Add the channel dimension"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Most of MONAI's image transformations assume that the input data has the shape:\n",
+    "\n",
+    "`[num_channels, spatial_dim_1, spatial_dim_2, ... ,spatial_dim_n]`\n",
+    "\n",
+    "so that they could be interpreted consistently (as \"channel-first\" is commonly used in PyTorch).\n",
+    "\n",
+    "Here the input image has shape `(512, 512, 55)` which isn't in the acceptable shape (missing the channel dimension),\n",
+    "\n",
+    "we therefore create a transform which is called to updat the shape:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "image shape (1, 512, 512, 55)\n"
+     ]
+    }
+   ],
+   "source": [
+    "add_channel = AddChanneld(keys=['image', 'label'])\n",
+    "datac_dict = add_channel(data_dict)\n",
+    "print('image shape', datac_dict['image'].shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we are ready to do some intensity and spatial transforms."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Resample to a consistent voxel size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The input volumes might have different voxel sizes.\n",
+    "\n",
+    "The following transform is created to normlise the volumes to have (1.5, 1.5, 5.) millimetre voxel size.\n",
+    "\n",
+    "The transform is set to read the original voxel size information from `data_dict['image.affine']`,\n",
+    "which is from the corresponding NIfTI file, loaded earlier by `LoadNiftid`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spacing = Spacingd(keys=['image', 'label'], \n",
+    "                   pixdim=(1.5, 1.5, 5.), interp_order=(2, 0), mode='nearest')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "image shape: (1, 334, 334, 55)\n",
+      "label shape: (1, 334, 334, 55)\n",
+      "image affine after Spacing\n",
+      " [[   1.5           0.            0.         -499.02319336]\n",
+      " [   0.            1.5           0.         -499.02319336]\n",
+      " [   0.            0.            5.            0.        ]\n",
+      " [   0.            0.            0.            1.        ]]\n",
+      "label affine after Spacing\n",
+      " [[   1.5           0.            0.         -499.02319336]\n",
+      " [   0.            1.5           0.         -499.02319336]\n",
+      " [   0.            0.            5.            0.        ]\n",
+      " [   0.            0.            0.            1.        ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_dict = spacing(datac_dict)\n",
+    "print('image shape:', data_dict['image'].shape)\n",
+    "print('label shape:', data_dict['label'].shape)\n",
+    "print('image affine after Spacing\\n', data_dict['image.affine']) \n",
+    "print('label affine after Spacing\\n', data_dict['label.affine'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To track the spacing changes, the data_dict was updated by `Spacingd`:\n",
+    "\n",
+    "- An `image.original_affine` key is added to the `data_dict`, logs the original affine.\n",
+    "\n",
+    "- An `image.affine` key is updated to have the current affine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "image, label = data_dict['image'], data_dict['label']\n",
+    "plt.figure('visualise', (8, 4))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.title(\"image\")\n",
+    "plt.imshow(image[0, :, :, 30], cmap='gray')\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.title(\"label\")\n",
+    "plt.imshow(label[0, :, :, 30])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reorientation to a designated axes codes\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sometimes it is nice to have all the input volumes in a consistent axes orientation.\n",
+    "\n",
+    "The default axis labels are Left (L), Right (R), Posterior (P), Anterior (A), Inferior (I), Superior (S).\n",
+    "\n",
+    "The following transform is created to reorientate the volumes to have 'Posterior, Left, Inferior' (PLI) orientation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spacing = Orientationd(keys=['image', 'label'], axcodes='PLI')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "image shape: (1, 334, 334, 55)\n",
+      "label shape: (1, 334, 334, 55)\n",
+      "image affine after Spacing\n",
+      " [[  0.          -1.5          0.           0.47680664]\n",
+      " [ -1.5          0.           0.           0.47680664]\n",
+      " [  0.           0.          -5.         270.        ]\n",
+      " [  0.           0.           0.           1.        ]]\n",
+      "label affine after Spacing\n",
+      " [[  0.          -1.5          0.           0.47680664]\n",
+      " [ -1.5          0.           0.           0.47680664]\n",
+      " [  0.           0.          -5.         270.        ]\n",
+      " [  0.           0.           0.           1.        ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_dict = spacing(data_dict)\n",
+    "print('image shape:', data_dict['image'].shape)\n",
+    "print('label shape:', data_dict['label'].shape)\n",
+    "print('image affine after Spacing\\n', data_dict['image.affine']) \n",
+    "print('label affine after Spacing\\n', data_dict['label.affine'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "image, label = data_dict['image'], data_dict['label']\n",
+    "plt.figure('visualise', (8, 4))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.title(\"image\")\n",
+    "plt.imshow(image[0, :, :, 30], cmap='gray')\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.title(\"label\")\n",
+    "plt.imshow(label[0, :, :, 30])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Random affine transformation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following affine transformation is defined to output a (300, 300, 50) image patch.\n",
+    "\n",
+    "The patch location is randomly chosen in a range of (-40, 40), (-40, 40), (-2, 2) in x, y, and z axes respectively.\n",
+    "The translation is relative to the image centre.\n",
+    "\n",
+    "The 3D rotation angle is randomly chosen from (-45, 45) degrees around the z axis, and 5 degrees around x and y axes.\n",
+    "\n",
+    "The random scaling factor is randomly chosen from (1.0 - 0.15, 1.0 + 0.15) along each axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rand_affine = RandAffined(keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=1.0,\n",
+    "                          spatial_size=(300, 300, 50),\n",
+    "                          translate_range=(40, 40, 2),\n",
+    "                          rotate_range=(np.pi/36, np.pi/36, np.pi*4),\n",
+    "                          scale_range=(0.15, 0.15, 0.15),\n",
+    "                          padding_mode='border')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can rerun this cell to generate a different randomised version of the original image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "image shape torch.Size([1, 300, 300, 50])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "affined_data_dict = rand_affine(data_dict)\n",
+    "print('image shape', affined_data_dict['image'].shape)\n",
+    "\n",
+    "image, label = affined_data_dict['image'][0], affined_data_dict['label'][0]\n",
+    "plt.figure('visualise', (12, 6))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.title(\"image\")\n",
+    "plt.imshow(image[:, :, 15], cmap='gray')\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.title(\"label\")\n",
+    "plt.imshow(label[:, :, 15], cmap='gray')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Random elastic deformation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly, the following elastic deformation is defined to output a (300, 300, 10) image patch.\n",
+    "\n",
+    "The image is resampled from a combination of affine transformations and elastic deformations.\n",
+    "\n",
+    "`sigma_range` controls the smoothness of the deformation (larger than 15 could be slow on CPU)\n",
+    "\n",
+    "`magnitude_rnage` controls the amplitude of the deformation (large than 500, the image becomes unrealistic)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rand_elastic = Rand3DElasticd(\n",
+    "    keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=1.0,\n",
+    "    sigma_range=(5, 8),\n",
+    "    magnitude_range=(100, 200),\n",
+    "    spatial_size=(300, 300, 10),\n",
+    "    translate_range=(50, 50, 2),\n",
+    "    rotate_range=(np.pi/36, np.pi/36, np.pi*2),\n",
+    "    scale_range=(0.15, 0.15, 0.15),\n",
+    "    padding_mode='border')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can rerun this cell to generate a different randomised version of the original image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "image shape (1, 300, 300, 10)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "deformed_data_dict = rand_elastic(data_dict)\n",
+    "print('image shape', deformed_data_dict['image'].shape)\n",
+    "\n",
+    "image, label = deformed_data_dict['image'][0], deformed_data_dict['label'][0]\n",
+    "plt.figure('visualise', (12, 6))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.title(\"image\")\n",
+    "plt.imshow(image[:, :, 5], cmap='gray')\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.title(\"label\")\n",
+    "plt.imshow(label[:, :, 5], cmap='gray')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/notebooks/nifti_read_example.ipynb b/examples/notebooks/nifti_read_example.ipynb
index f50f838156..9a90e054d7 100644
--- a/examples/notebooks/nifti_read_example.ipynb
+++ b/examples/notebooks/nifti_read_example.ipynb
@@ -17,7 +17,13 @@
     {
      "name": "stdout",
      "output_type": "stream",
-     "text": "MONAI version: 0.0.1\nPython version: 3.8.1 (default, Jan  8 2020, 22:29:32)  [GCC 7.3.0]\nNumpy version: 1.18.1\nPytorch version: 1.4.0\nIgnite version: 0.3.0\n"
+     "text": [
+      "MONAI version: 0.0.1\n",
+      "Python version: 3.5.6 |Anaconda, Inc.| (default, Aug 26 2018, 16:30:03)  [GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]\n",
+      "Numpy version: 1.18.2\n",
+      "Pytorch version: 1.4.0\n",
+      "Ignite version: 0.3.0\n"
+     ]
     }
    ],
    "source": [
@@ -35,56 +41,18 @@
     "\n",
     "import torch\n",
     "from torch.utils.data import DataLoader\n",
-    "import monai.transforms.compose as transforms\n",
+    "from monai.transforms.compose import Compose\n",
     "\n",
     "import monai\n",
     "\n",
-    "from monai.transforms.utils import rescale_array\n",
     "from monai.data.nifti_reader import NiftiDataset\n",
     "from monai.transforms import AddChannel, Transpose, Rescale, ToTensor, RandUniformPatch\n",
     "from monai.data.grid_dataset import GridPatchDataset\n",
+    "from monai.data.synthetic import create_test_image_3d\n",
     "\n",
     "monai.config.print_config()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Define a function for creating test images and segmentations:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def create_test_image_3d(height, width, depth, numObjs=12, radMax=30, noiseMax=0.0, numSegClasses=5):\n",
-    "    '''Return a noisy 3D image and segmentation.'''\n",
-    "    image = np.zeros((width, height,depth))\n",
-    "\n",
-    "    for i in range(numObjs):\n",
-    "        x = np.random.randint(radMax, width - radMax)\n",
-    "        y = np.random.randint(radMax, height - radMax)\n",
-    "        z = np.random.randint(radMax, depth - radMax)\n",
-    "        rad = np.random.randint(5, radMax)\n",
-    "        spy, spx, spz = np.ogrid[-x:width - x, -y:height - y, -z:depth - z]\n",
-    "        circle = (spx * spx + spy * spy + spz * spz) <= rad * rad\n",
-    "\n",
-    "        if numSegClasses > 1:\n",
-    "            image[circle] = np.ceil(np.random.random() * numSegClasses)\n",
-    "        else:\n",
-    "            image[circle] = np.random.random() * 0.5 + 0.5\n",
-    "\n",
-    "    labels = np.ceil(image).astype(np.int32)\n",
-    "\n",
-    "    norm = np.random.uniform(0, numSegClasses * noiseMax, size=image.shape)\n",
-    "    noisyimage = rescale_array(np.maximum(image, norm))\n",
-    "\n",
-    "    return noisyimage, labels"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -94,14 +62,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "tempdir = tempfile.mkdtemp()\n",
     "\n",
     "for i in range(5):\n",
-    "    im, seg = create_test_image_3d(256,256,256)\n",
+    "    im, seg = create_test_image_3d(128, 128, 128)\n",
     "    \n",
     "    n = nib.Nifti1Image(im, np.eye(4))\n",
     "    nib.save(n, os.path.join(tempdir, 'im%i.nii.gz'%i))\n",
@@ -119,33 +87,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
-     "text": "torch.Size([5, 1, 64, 64, 64]) torch.Size([5, 256, 256, 256])\n"
+     "text": [
+      "torch.Size([5, 1, 64, 64, 64]) torch.Size([5, 1, 64, 64, 64])\n"
+     ]
     }
    ],
    "source": [
     "images = sorted(glob(os.path.join(tempdir,'im*.nii.gz')))\n",
     "segs = sorted(glob(os.path.join(tempdir,'seg*.nii.gz')))\n",
     "\n",
-    "imtrans=transforms.Compose([\n",
+    "imtrans = Compose([\n",
     "    Rescale(),\n",
     "    AddChannel(),\n",
     "    RandUniformPatch((64, 64, 64)),\n",
     "    ToTensor()\n",
     "])    \n",
     "\n",
-    "segtrans=transforms.Compose([\n",
+    "segtrans = Compose([\n",
     "    AddChannel(),\n",
     "    RandUniformPatch((64, 64, 64)),\n",
     "    ToTensor()\n",
     "])    \n",
     "    \n",
-    "ds = NiftiDataset(images, segs, imtrans, segtrans)\n",
+    "ds = NiftiDataset(images, segs, transform=imtrans, seg_transform=segtrans)\n",
     "\n",
     "loader = DataLoader(ds, batch_size=10, num_workers=2, pin_memory=torch.cuda.is_available())\n",
     "im, seg = monai.utils.misc.first(loader)\n",
@@ -161,28 +131,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
-     "text": "torch.Size([10, 1, 64, 64, 64]) torch.Size([10, 256, 64, 64])\n"
+     "text": [
+      "torch.Size([10, 1, 64, 64, 64]) torch.Size([10, 1, 64, 64, 64])\n"
+     ]
     }
    ],
    "source": [
-    "imtrans=transforms.Compose([\n",
+    "imtrans = Compose([\n",
     "    Rescale(),\n",
     "    AddChannel(),\n",
     "    ToTensor()\n",
     "])    \n",
     "\n",
-    "segtrans=transforms.Compose([\n",
+    "segtrans = Compose([\n",
     "    AddChannel(),\n",
     "    ToTensor()\n",
     "])    \n",
     "    \n",
-    "ds = NiftiDataset(images, segs, imtrans, segtrans)\n",
+    "ds = NiftiDataset(images, segs, transform=imtrans, seg_transform=segtrans)\n",
     "ds = GridPatchDataset(ds, (64, 64, 64))\n",
     "\n",
     "loader = DataLoader(ds, batch_size=10, num_workers=2, pin_memory=torch.cuda.is_available())\n",
@@ -192,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -223,9 +195,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.1-final"
+   "version": "3.5.6"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/examples/notebooks/spleen_segmentation_3d.ipynb b/examples/notebooks/spleen_segmentation_3d.ipynb
index 841803419f..ac13dfc6fe 100644
--- a/examples/notebooks/spleen_segmentation_3d.ipynb
+++ b/examples/notebooks/spleen_segmentation_3d.ipynb
@@ -70,7 +70,8 @@
     "import monai\n",
     "import monai.transforms.compose as transforms\n",
     "from monai.transforms.composables import \\\n",
-    "    LoadNiftid, AddChanneld, ScaleIntensityRanged, RandCropByPosNegLabeld, RandAffined\n",
+    "    LoadNiftid, AddChanneld, ScaleIntensityRanged, RandCropByPosNegLabeld, \\\n",
+    "    RandAffined, Spacingd, Orientationd\n",
     "from monai.data.utils import list_data_collate\n",
     "from monai.utils.sliding_window_inference import sliding_window_inference\n",
     "from monai.metrics.compute_meandice import compute_meandice\n",
@@ -115,6 +116,8 @@
     "train_transforms = transforms.Compose([\n",
     "    LoadNiftid(keys=['image', 'label']),\n",
     "    AddChanneld(keys=['image', 'label']),\n",
+    "    Spacingd(keys=['image', 'label'], pixdim=(1.5, 1.5, 2.), interp_order=(3, 0)),\n",
+    "    Orientationd(keys=['image', 'label'], axcodes='RAS'),\n",
     "    ScaleIntensityRanged(keys=['image'], a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True),\n",
     "    # randomly crop out patch samples from big image based on pos / neg ratio\n",
     "    # the image centers of negative samples must be in valid image area\n",
@@ -127,6 +130,8 @@
     "val_transforms = transforms.Compose([\n",
     "    LoadNiftid(keys=['image', 'label']),\n",
     "    AddChanneld(keys=['image', 'label']),\n",
+    "    Spacingd(keys=['image', 'label'], pixdim=(1.5, 1.5, 2.), interp_order=(3, 0)),\n",
+    "    Orientationd(keys=['image', 'label'], axcodes='RAS'),\n",
     "    ScaleIntensityRanged(keys=['image'], a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True)\n",
     "])"
    ]
@@ -516,4 +521,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/examples/notebooks/transforms_demo_2d.ipynb b/examples/notebooks/transforms_demo_2d.ipynb
index 9ced3e86b2..b9c854bbeb 100644
--- a/examples/notebooks/transforms_demo_2d.ipynb
+++ b/examples/notebooks/transforms_demo_2d.ipynb
@@ -29,6 +29,15 @@
     "K. Sirinukunwattana, J. P. W. Pluim, H. Chen, X Qi, P. Heng, Y. Guo, L. Wang, B. J. Matuszewski, E. Bruni, U. Sanchez, A. Böhm, O. Ronneberger, B. Ben Cheikh, D. Racoceanu, P. Kainz, M. Pfeiffer, M. Urschler, D. R. J. Snead, N. M. Rajpoot, \"Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest\" http://arxiv.org/abs/1603.00275 [Preprint]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`wget https://warwick.ac.uk/fac/sci/dcs/research/tia/glascontest/download/warwick_qu_dataset_released_2016_07_08.zip`\n",
+    "\n",
+    "`unzip warwick_qu_dataset_released_2016_07_08.zip`"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -39,8 +48,8 @@
      "output_type": "stream",
      "text": [
       "MONAI version: 0.0.1\n",
-      "Python version: 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31)  [GCC 7.3.0]\n",
-      "Numpy version: 1.18.1\n",
+      "Python version: 3.5.6 |Anaconda, Inc.| (default, Aug 26 2018, 16:30:03)  [GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]\n",
+      "Numpy version: 1.18.2\n",
       "Pytorch version: 1.4.0\n",
       "Ignite version: 0.3.0\n"
      ]
@@ -87,7 +96,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -114,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -143,22 +152,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fa3c6859550>"
+       "<matplotlib.image.AxesImage at 0x7fda28395cc0>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -184,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -212,22 +221,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fa3c6775ef0>"
+       "<matplotlib.image.AxesImage at 0x7fda18a53908>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -243,6 +252,13 @@
     "axes[0].imshow(np.moveaxis(new_img.astype(int), 0, -1))\n",
     "axes[1].imshow(new_seg[0].astype(int))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -261,7 +277,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.5.6"
   }
  },
  "nbformat": 4,
diff --git a/examples/notebooks/unet_segmentation_3d_ignite.ipynb b/examples/notebooks/unet_segmentation_3d_ignite.ipynb
index 00dceb150a..ad2e1d742c 100644
--- a/examples/notebooks/unet_segmentation_3d_ignite.ipynb
+++ b/examples/notebooks/unet_segmentation_3d_ignite.ipynb
@@ -9,21 +9,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MONAI version: 0.0.1\n",
-      "Python version: 3.7.4 (default, Aug 13 2019, 20:35:49)  [GCC 7.3.0]\n",
-      "Numpy version: 1.17.2+intel.0\n",
-      "Pytorch version: 1.4.0\n",
-      "Ignite version: 0.3.0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "import sys\n",
@@ -34,35 +22,36 @@
     "import nibabel as nib\n",
     "import numpy as np\n",
     "import torch\n",
-    "from torch.utils.tensorboard import SummaryWriter\n",
     "from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator\n",
-    "from ignite.handlers import ModelCheckpoint, EarlyStopping\n",
+    "from ignite.handlers import ModelCheckpoint\n",
     "from torch.utils.data import DataLoader\n",
     "\n",
     "import monai\n",
     "import monai.transforms.compose as transforms\n",
     "\n",
     "from monai.data.nifti_reader import NiftiDataset\n",
-    "from monai.transforms import (AddChannel, Rescale, ToTensor, RandUniformPatch)\n",
+    "from monai.transforms import (AddChannel, Rescale, Resize, ToTensor, RandUniformPatch)\n",
     "from monai.handlers.stats_handler import StatsHandler\n",
+    "from monai.handlers.tensorboard_handlers import TensorBoardStatsHandler, TensorBoardImageHandler\n",
     "from monai.handlers.mean_dice import MeanDice\n",
-    "from monai.visualize import img2tensorboard\n",
     "from monai.data.synthetic import create_test_image_3d\n",
     "from monai.handlers.utils import stopping_fn_from_metric\n",
+    "from monai.networks.utils import predict_segmentation\n",
     "\n",
-    "monai.config.print_config()"
+    "monai.config.print_config()\n",
+    "logging.basicConfig(stream=sys.stdout, level=logging.INFO)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Setup Test data"
+    "## Setup demo data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,17 +77,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([10, 1, 96, 96, 96]) torch.Size([10, 1, 96, 96, 96])\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "images = sorted(glob(os.path.join(tempdir, 'im*.nii.gz')))\n",
     "segs = sorted(glob(os.path.join(tempdir, 'seg*.nii.gz')))\n",
@@ -132,12 +113,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "lr = 1e-5\n",
-    "\n",
     "# Create UNet, DiceLoss and Adam optimizer.\n",
     "net = monai.networks.nets.UNet(\n",
     "    dimensions=3,\n",
@@ -149,6 +128,7 @@
     ")\n",
     "\n",
     "loss = monai.losses.DiceLoss(do_sigmoid=True)\n",
+    "lr = 1e-3\n",
     "opt = torch.optim.Adam(net.parameters(), lr)"
    ]
   },
@@ -161,18 +141,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Since network outputs logits and segmentation, we need a custom function.\n",
-    "def _loss_fn(i, j):\n",
-    "    return loss(i[0], j)\n",
-    "\n",
     "# Create trainer\n",
-    "device = torch.device(\"cuda:0\")\n",
-    "trainer = create_supervised_trainer(net, opt, _loss_fn, device, False,\n",
-    "                                    output_transform=lambda x, y, y_pred, loss: [y_pred, loss.item(), y])"
+    "device = torch.device(\"cpu:0\")\n",
+    "trainer = create_supervised_trainer(net, opt, loss, device, False)"
    ]
   },
   {
@@ -184,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -194,42 +169,16 @@
     "trainer.add_event_handler(event_name=Events.EPOCH_COMPLETED,\n",
     "                          handler=checkpoint_handler,\n",
     "                          to_save={'net': net, 'opt': opt})\n",
-    "train_stats_handler = StatsHandler(output_transform=lambda x: x[1])\n",
+    "# StatsHandler prints loss at every iteration and print metrics at every epoch,\n",
+    "# we don't set metrics for trainer here, so just print loss, user can also customize print functions\n",
+    "# and can use output_transform to convert engine.state.output if it's not a loss value\n",
+    "train_stats_handler = StatsHandler(name='trainer')\n",
     "train_stats_handler.attach(trainer)\n",
     "\n",
-    "writer = SummaryWriter()\n",
-    "\n",
-    "@trainer.on(Events.EPOCH_COMPLETED)\n",
-    "def log_training_loss(engine):\n",
-    "    # log loss to tensorboard with second item of engine.state.output, loss.item() from output_transform\n",
-    "    writer.add_scalar('Loss/train', engine.state.output[1], engine.state.epoch)\n",
-    "\n",
-    "    # tensor of ones to use where for converting labels to zero and ones\n",
-    "    ones = torch.ones(engine.state.batch[1][0].shape, dtype=torch.int32)\n",
-    "    first_output_tensor = engine.state.output[0][1][0].detach().cpu()\n",
-    "    # log model output to tensorboard, as three dimensional tensor with no channels dimension\n",
-    "    img2tensorboard.add_animated_gif_no_channels(writer, \"first_output_final_batch\", first_output_tensor, 64,\n",
-    "                                                 255, engine.state.epoch)\n",
-    "    # get label tensor and convert to single class\n",
-    "    first_label_tensor = torch.where(engine.state.batch[1][0] > 0, ones, engine.state.batch[1][0])\n",
-    "    # log label tensor to tensorboard, there is a channel dimension when getting label from batch\n",
-    "    img2tensorboard.add_animated_gif(writer, \"first_label_final_batch\", first_label_tensor, 64,\n",
-    "                                     255, engine.state.epoch)\n",
-    "    second_output_tensor = engine.state.output[0][1][1].detach().cpu()\n",
-    "    img2tensorboard.add_animated_gif_no_channels(writer, \"second_output_final_batch\", second_output_tensor, 64,\n",
-    "                                                 255, engine.state.epoch)\n",
-    "    second_label_tensor = torch.where(engine.state.batch[1][1] > 0, ones, engine.state.batch[1][1])\n",
-    "    img2tensorboard.add_animated_gif(writer, \"second_label_final_batch\", second_label_tensor, 64,\n",
-    "                                     255, engine.state.epoch)\n",
-    "    third_output_tensor = engine.state.output[0][1][2].detach().cpu()\n",
-    "    img2tensorboard.add_animated_gif_no_channels(writer, \"third_output_final_batch\", third_output_tensor, 64,\n",
-    "                                                 255, engine.state.epoch)\n",
-    "    third_label_tensor = torch.where(engine.state.batch[1][2] > 0, ones, engine.state.batch[1][2])\n",
-    "    img2tensorboard.add_animated_gif(writer, \"third_label_final_batch\", third_label_tensor, 64,\n",
-    "                                     255, engine.state.epoch)\n",
-    "    engine.logger.info(\"Epoch[%s] Loss: %s\", engine.state.epoch, engine.state.output[1])\n",
     "\n",
-    "\n"
+    "# TensorBoardStatsHandler plots loss at every iteration and plots metrics at every epoch, same as StatsHandler\n",
+    "train_tensorboard_stats_handler = TensorBoardStatsHandler()\n",
+    "train_tensorboard_stats_handler.attach(trainer)"
    ]
   },
   {
@@ -241,44 +190,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "### optional section for model validation during training\n",
-    "# Set parameters for validation\n",
     "validation_every_n_epochs = 1\n",
+    "# Set parameters for validation\n",
     "metric_name = 'Mean_Dice'\n",
-    "\n",
     "# add evaluation metric to the evaluator engine\n",
-    "val_metrics = {metric_name: MeanDice(add_sigmoid=True)}\n",
-    "evaluator = create_supervised_evaluator(net, val_metrics, device, True,\n",
-    "                                        output_transform=lambda x, y, y_pred: (y_pred[0], y))\n",
+    "val_metrics = {metric_name: MeanDice(add_sigmoid=True, to_onehot_y=False)}\n",
     "\n",
-    "# Add stats event handler to print validation stats via evaluator\n",
-    "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
-    "val_stats_handler = StatsHandler(lambda x: None)\n",
-    "val_stats_handler.attach(evaluator)\n",
-    "\n",
-    "# Add early stopping handler to evaluator.\n",
-    "early_stopper = EarlyStopping(patience=4,\n",
-    "                              score_function=stopping_fn_from_metric(metric_name),\n",
-    "                              trainer=trainer)\n",
-    "evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=early_stopper)\n",
+    "# ignite evaluator expects batch=(img, seg) and returns output=(y_pred, y) at every iteration,\n",
+    "# user can add output_transform to return other values\n",
+    "evaluator = create_supervised_evaluator(net, val_metrics, device, True)\n",
     "\n",
     "# create a validation data loader\n",
-    "val_ds = NiftiDataset(images[-20:], segs[-20:], transform=imtrans, seg_transform=segtrans)\n",
-    "val_loader = DataLoader(ds, batch_size=5, num_workers=8, pin_memory=torch.cuda.is_available())\n",
+    "val_imtrans = transforms.Compose([\n",
+    "    Rescale(),\n",
+    "    AddChannel(),\n",
+    "    Resize((96, 96, 96))\n",
+    "])\n",
+    "val_segtrans = transforms.Compose([\n",
+    "    AddChannel(),\n",
+    "    Resize((96, 96, 96))\n",
+    "])\n",
+    "val_ds = NiftiDataset(images[-20:], segs[-20:], transform=val_imtrans, seg_transform=val_segtrans)\n",
+    "val_loader = DataLoader(val_ds, batch_size=5, num_workers=8, pin_memory=torch.cuda.is_available())\n",
     "\n",
     "\n",
     "@trainer.on(Events.EPOCH_COMPLETED(every=validation_every_n_epochs))\n",
     "def run_validation(engine):\n",
     "    evaluator.run(val_loader)\n",
     "\n",
-    "@evaluator.on(Events.EPOCH_COMPLETED)\n",
-    "def log_metrics_to_tensorboard(engine):\n",
-    "    for name, value in engine.state.metrics.items():\n",
-    "        writer.add_scalar(f'Metrics/{name}', value, trainer.state.epoch)\n"
+    "\n",
+    "# Add stats event handler to print validation stats via evaluator\n",
+    "val_stats_handler = StatsHandler(\n",
+    "    name='evaluator',\n",
+    "    output_transform=lambda x: None,  # no need to print loss value, so disable per iteration output\n",
+    "    global_epoch_transform=lambda x: trainer.state.epoch)  # fetch global epoch number from trainer\n",
+    "val_stats_handler.attach(evaluator)\n",
+    "\n",
+    "# add handler to record metrics to TensorBoard at every validation epoch\n",
+    "val_tensorboard_stats_handler = TensorBoardStatsHandler(\n",
+    "    output_transform=lambda x: None,  # no need to plot loss value, so disable per iteration output\n",
+    "    global_epoch_transform=lambda x: trainer.state.epoch)  # fetch global epoch number from trainer\n",
+    "val_tensorboard_stats_handler.attach(evaluator)\n",
+    "\n",
+    "# add handler to draw the first image and the corresponding label and model output in the last batch\n",
+    "# here we draw the 3D output as GIF format along Depth axis, at every validation epoch\n",
+    "val_tensorboard_image_handler = TensorBoardImageHandler(\n",
+    "    batch_transform=lambda batch: (batch[0], batch[1]),\n",
+    "    output_transform=lambda output: predict_segmentation(output[0]),\n",
+    "    global_iter_transform=lambda x: trainer.state.epoch\n",
+    ")\n",
+    "evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=val_tensorboard_image_handler)"
    ]
   },
   {
@@ -290,204 +256,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=30.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Loss: 0.6422698497772217\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.3222 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:01\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[2] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[2] Loss: 0.6460620164871216\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.3284 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[3] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[3] Loss: 0.6517763137817383\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.3345 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:01\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[4] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[4] Loss: 0.6451399326324463\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.3406 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[5] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[5] Loss: 0.6444500684738159\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.3469 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:01\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[6] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[6] Loss: 0.6436276435852051\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.3534 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:01\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[7] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[7] Loss: 0.6465097069740295\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.3602 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[8] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[8] Loss: 0.6543605327606201\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.3676 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:01\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[9] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[9] Loss: 0.6376665234565735\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.3756 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:01\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[10] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[10] Loss: 0.6404213905334473\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.3842 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:01\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[11] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[11] Loss: 0.6427902579307556\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.3931 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:01\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[12] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[12] Loss: 0.6375727653503418\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.4024 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:01\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[13] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[13] Loss: 0.640845537185669\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.4119 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:01\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[14] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[14] Loss: 0.6324806809425354\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:00\n",
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-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[15] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[15] Loss: 0.6381043195724487\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
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-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.4312 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[16] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[16] Loss: 0.6361473202705383\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.4408 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:01\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[17] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[17] Loss: 0.6266491413116455\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
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-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:01\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[18] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[18] Loss: 0.6219738721847534\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
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-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.4594 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:01\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[19] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[19] Loss: 0.6317167282104492\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.4687 \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[20] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[20] Loss: 0.6311004161834717\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.4778 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[21] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[21] Loss: 0.6252765655517578\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
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-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.4869 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[22] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[22] Loss: 0.6269791126251221\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.4957 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:01\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[23] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[23] Loss: 0.6195886731147766\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
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-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.5044 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[24] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[24] Loss: 0.6242675185203552\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
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-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.5126 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[25] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[25] Loss: 0.6193283200263977\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
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-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:01\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[26] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[26] Loss: 0.6183103322982788\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:00\n",
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-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[27] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[27] Loss: 0.6169862747192383\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.5337 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[28] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[28] Loss: 0.6121048331260681\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
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-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.5394 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:01\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[29] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[29] Loss: 0.618910551071167\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.5445 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:01\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[30] Complete. Time taken: 00:00:02\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[30] Loss: 0.6069970726966858\n",
-      "INFO:ignite.engine.engine.Engine:Engine run starting with max_epochs=1.\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Complete. Time taken: 00:00:00\n",
-      "INFO:ignite.engine.engine.Engine:Epoch[1] Metrics -- Mean_Dice: 0.5492 \n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:00:01\n",
-      "INFO:ignite.engine.engine.Engine:Engine run complete. Time taken 00:02:09\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# create a training data loader\n",
     "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
@@ -508,35 +279,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "      <iframe id=\"tensorboard-frame-44dcda6a797d76de\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
-       "      </iframe>\n",
-       "      <script>\n",
-       "        (function() {\n",
-       "          const frame = document.getElementById(\"tensorboard-frame-44dcda6a797d76de\");\n",
-       "          const url = new URL(\"/\", window.location);\n",
-       "          url.port = 6008;\n",
-       "          frame.src = url;\n",
-       "        })();\n",
-       "      </script>\n",
-       "  "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "log_dir = writer.get_logdir()\n",
+    "log_dir = './runs'  # by default TensorBoard logs go into './runs'\n",
     "\n",
     "%load_ext tensorboard\n",
     "%tensorboard --logdir $log_dir"
@@ -568,7 +315,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.5.6"
   }
  },
  "nbformat": 4,
diff --git a/monai/data/nifti_saver.py b/monai/data/nifti_saver.py
index 924f9983f0..bddacf5172 100644
--- a/monai/data/nifti_saver.py
+++ b/monai/data/nifti_saver.py
@@ -99,7 +99,7 @@ def save(self, data, meta_data=None):
         filename = '{}{}'.format(filename, self.output_ext)
         # change data to "channel last" format and write to nifti format file
         data = np.moveaxis(data, 0, -1)
-        write_nifti(data, affine, filename, original_affine, dtype=self.dtype or data.dtype)
+        write_nifti(data, filename, affine, original_affine, dtype=self.dtype or data.dtype)
 
     def save_batch(self, batch_data, meta_data=None):
         """Save a batch of data into Nifti format files.
diff --git a/monai/data/nifti_writer.py b/monai/data/nifti_writer.py
index ac1d5b4840..21cae7727a 100644
--- a/monai/data/nifti_writer.py
+++ b/monai/data/nifti_writer.py
@@ -13,7 +13,7 @@
 import nibabel as nib
 
 
-def write_nifti(data, affine, file_name, target_affine=None, dtype=np.float32):
+def write_nifti(data, file_name, affine=None, target_affine=None, dtype=np.float32):
     """Write numpy data into nifti files to disk.
 
     Args:
diff --git a/monai/data/utils.py b/monai/data/utils.py
index a19916ac7c..e47c75824d 100644
--- a/monai/data/utils.py
+++ b/monai/data/utils.py
@@ -11,10 +11,11 @@
 
 import warnings
 import math
+import nibabel as nib
 from itertools import starmap, product
 from torch.utils.data._utils.collate import default_collate
 import numpy as np
-from monai.transforms.utils import ensure_tuple_size
+from monai.transforms.utils import ensure_tuple_size, to_affine_nd
 
 
 def get_random_patch(dims, patch_size, rand_state=None):
@@ -243,13 +244,81 @@ def rectify_header_sform_qform(img_nii):
             img_nii.set_qform(img_nii.get_sform())
             return img_nii
 
-    norm_affine = np.sqrt(np.sum(np.square(img_nii.affine[:, :3]), 0))
-    to_divide = np.tile(np.expand_dims(np.append(norm_affine, 1), axis=1), [1, 4])
-    pixdim = np.append(pixdim, [1.] * (4 - len(pixdim)))
-    to_multiply = np.tile(np.expand_dims(pixdim, axis=1), [1, 4])
-    affine = img_nii.affine / to_divide.T * to_multiply.T
-    warnings.warn('Modifying image affine from {} to {}'.format(img_nii.affine, affine))
+    norm = np.sqrt(np.sum(np.square(img_nii.affine[:d, :d]), 0))
+    warnings.warn('Modifying image pixdim from {} to {}'.format(pixdim, norm))
 
-    img_nii.set_sform(affine)
-    img_nii.set_qform(affine)
+    img_nii.header.set_zooms(norm)
     return img_nii
+
+
+def zoom_affine(affine, scale, diagonal=True):
+    """
+    To make column norm of `affine` the same as `scale`.  if diagonal is False,
+    returns an affine that combines orthogonal rotation and the new scale.
+    This is done by first decomposing`affine`, then setting the zoom factors to
+    `scale`, and composing a new affine; the shearing factors are removed.  If
+    diagonal is True, returns an diagonal matrix, the scaling factors are set
+    to the diagonal elements.  This function always return an affine with zero
+    translations.
+
+    Args:
+        affine (nxn matrix): a square matrix.
+        scale (sequence of floats): new scaling factor along each dimension.
+        diagnonal (bool): whether to return a diagnoal scaling matrix.
+            Defaults to True.
+
+    returns:
+        the updated `n x n` affine.
+    """
+    affine = np.array(affine, dtype=float, copy=True)
+    if len(affine) != len(affine[0]):
+        raise ValueError('affine should be a square matrix')
+    scale = np.array(scale, dtype=float, copy=True)
+    if np.any(scale <= 0):
+        raise ValueError('scale must be a sequence of positive numbers.')
+    d = len(affine) - 1
+    if len(scale) < d:  # defaults based on affine
+        norm = np.sqrt(np.sum(np.square(affine), 0))[:-1]
+        scale = np.append(scale, norm[len(scale):])
+    scale = scale[:d]
+    scale[scale == 0] = 1.
+    if diagonal:
+        return np.diag(np.append(scale, [1.]))
+    rzs = affine[:-1, :-1]  # rotation zoom scale
+    zs = np.linalg.cholesky(rzs.T @ rzs).T
+    rotation = rzs @ np.linalg.inv(zs)
+    s = np.sign(np.diag(zs)) * np.abs(scale)
+    # construct new affine with rotation and zoom
+    new_affine = np.eye(len(affine))
+    new_affine[:-1, :-1] = rotation @ np.diag(s)
+    return new_affine
+
+
+def compute_shape_offset(spatial_shape, in_affine, out_affine):
+    """
+    Given input and target affine, compute appropriate shapes
+    in the target space based on the input array's shape.
+    This function also returns the offset to put the shape
+    in a good position with respect to the world coordinate system.
+    """
+    shape = np.array(spatial_shape, copy=True, dtype=float)
+    sr = len(shape)
+    in_affine = to_affine_nd(sr, in_affine)
+    out_affine = to_affine_nd(sr, out_affine)
+    in_coords = [(0., dim - 1.) for dim in shape]
+    corners = np.asarray(np.meshgrid(*in_coords, indexing='ij')).reshape((len(shape), -1))
+    corners = np.concatenate((corners, np.ones_like(corners[:1])))
+    corners = in_affine @ corners
+    corners_out = np.linalg.inv(out_affine) @ corners
+    corners_out = corners_out[:-1] / corners_out[-1]
+    out_shape = np.ceil(np.max(corners_out, 1) - np.min(corners_out, 1)) + 1.
+    if np.allclose(nib.io_orientation(in_affine),
+                   nib.io_orientation(out_affine)):
+        # same orientation, get translate from the origin
+        offset = in_affine @ ([0] * sr + [1])
+        offset = offset[:-1] / offset[-1]
+    else:
+        # different orientation, the min is the origin
+        corners = corners[:-1] / corners[-1]
+        offset = np.min(corners, 1)
+    return out_shape.astype(int), offset
diff --git a/monai/transforms/composables.py b/monai/transforms/composables.py
index 88cebc41d0..5edc286486 100644
--- a/monai/transforms/composables.py
+++ b/monai/transforms/composables.py
@@ -15,20 +15,19 @@
 Class names are ended with 'd' to denote dictionary-based transforms.
 """
 
-import torch
 import numpy as np
+import torch
+
 import monai
 from monai.data.utils import get_random_patch, get_valid_patch_size
 from monai.networks.layers.simplelayers import GaussianFilter
-from monai.transforms.compose import Randomizable, MapTransform
-from monai.transforms.transforms import (LoadNifti, AsChannelFirst, Orientation,
-                                         AddChannel, Spacing, Rotate90, SpatialCrop,
-                                         RandAffine, Rand2DElastic, Rand3DElastic,
-                                         Rescale, Resize, Flip, Rotate, Zoom,
-                                         NormalizeIntensity, ScaleIntensityRange)
-from monai.utils.misc import ensure_tuple
-from monai.transforms.utils import generate_pos_neg_label_crop_centers, create_grid
+from monai.transforms.compose import MapTransform, Randomizable
+from monai.transforms.transforms import (AddChannel, AsChannelFirst, Flip, LoadNifti, NormalizeIntensity, Orientation,
+                                         Rand2DElastic, Rand3DElastic, RandAffine, Rescale, Resize, Rotate, Rotate90,
+                                         ScaleIntensityRange, Spacing, SpatialCrop, Zoom)
+from monai.transforms.utils import (create_grid, generate_pos_neg_label_crop_centers)
 from monai.utils.aliases import alias
+from monai.utils.misc import ensure_tuple
 
 export = monai.utils.export("monai.transforms")
 
@@ -37,39 +36,75 @@
 @alias('SpacingD', 'SpacingDict')
 class Spacingd(MapTransform):
     """
-    dictionary-based wrapper of :py:class:`monai.transforms.transforms.Spacing`.
+    Dictionary-based wrapper of :py:class:`monai.transforms.transforms.Spacing`.
+
+    This transform assumes the ``data`` dictionary has a field for the input
+    data's affine.  The field is created by either ``meta_key_format.format(key,
+    'affine')`` or ``meta_key_format.format(key, 'original_affine')``.
+
+    After resampling the input array, this transform will write the affine
+    after resampling to the field ``meta_key_format.format(key, 'affine')``,
+    at the same time, if ``meta_key_format.format(key, 'original_affine')`` doesn't exist,
+    the field will be created and set to the affine before resampling.
+
+    if no affine is specified in the input data, defauting to "eye(4)".
+
+    see also:
+        :py:class:`monai.transforms.transforms.Spacing`
     """
 
-    def __init__(self, keys, affine_key, pixdim, interp_order=2, keep_shape=False, output_key='spacing'):
+    def __init__(self, keys, pixdim, diagonal=False, mode='constant', cval=0,
+                 interp_order=3, dtype=None, meta_key_format='{}.{}'):
         """
         Args:
-            affine_key (hashable): the key to the original affine.
-                The affine will be used to compute input data's pixdim.
             pixdim (sequence of floats): output voxel spacing.
+            diagonal (bool): whether to resample the input to have a diagonal affine matrix.
+                If True, the input data is resampled to the following affine::
+
+                    np.diag((pixdim_0, pixdim_1, pixdim_2, 1))
+
+                This effectively resets the volume to the world coordinate system (RAS+ in nibabel).
+                The original orientation, rotation, shearing are not preserved.
+
+                If False, the axes orientation, orthogonal rotation and
+                translations components from the original affine will be
+                preserved in the target affine. This option will not flip/swap
+                axes against the original ones.
+            mode (`reflect|constant|nearest|mirror|wrap`):
+                The mode parameter determines how the input array is extended beyond its boundaries.
+                Default is 'constant'.
+            cval (scalar): Value to fill past edges of input if mode is "constant". Default is 0.0.
             interp_order (int or sequence of ints): int: the same interpolation order
-                for all data indexed by `self,keys`; sequence of ints, should
+                for all data indexed by `self.keys`; sequence of ints, should
                 correspond to an interpolation order for each data item indexed
                 by `self.keys` respectively.
-            keep_shape (bool): whether to maintain the original spatial shape
-                after resampling. Defaults to False.
-            output_key (hashable): key to be added to the output dictionary to track
-                the pixdim status.
+            dtype (None or np.dtype): output array data type, defaults to None to use input data's dtype.
+            meta_key_format (str): key format to read/write affine matrices to the data dictionary.
         """
         MapTransform.__init__(self, keys)
-        self.affine_key = affine_key
-        self.spacing_transform = Spacing(pixdim, keep_shape=keep_shape)
+        self.spacing_transform = Spacing(pixdim, diagonal=diagonal, mode=mode, cval=cval, dtype=dtype)
         interp_order = ensure_tuple(interp_order)
         self.interp_order = interp_order \
             if len(interp_order) == len(self.keys) else interp_order * len(self.keys)
-        self.output_key = output_key
+        self.meta_key_format = meta_key_format
 
     def __call__(self, data):
         d = dict(data)
-        affine = d[self.affine_key]
-        original_pixdim, new_pixdim = None, None
         for key, interp in zip(self.keys, self.interp_order):
-            d[key], original_pixdim, new_pixdim = self.spacing_transform(d[key], affine, interp_order=interp)
-        d[self.output_key] = {'original_pixdim': original_pixdim, 'current_pixdim': new_pixdim}
+            affine_key = self.meta_key_format.format(key, 'affine')
+            original_key = self.meta_key_format.format(key, 'original_affine')
+            affine = d.get(affine_key, None)
+            if affine is None:
+                affine = d.get(original_key, None)
+            # resample array of each corresponding key
+            # using affine fetched from d[affine_key]
+            d[key], affine_, new_affine = self.spacing_transform(
+                data_array=d[key], original_affine=affine, interp_order=interp)
+            if d.get(original_key, None) is None:
+                # set the 'original_affine' field
+                d[original_key] = affine_
+            # set the 'affine' key
+            d[affine_key] = new_affine
         return d
 
 
@@ -77,37 +112,54 @@ def __call__(self, data):
 @alias('OrientationD', 'OrientationDict')
 class Orientationd(MapTransform):
     """
-    dictionary-based wrapper of :py:class:`monai.transforms.transforms.Orientation`.
+    Dictionary-based wrapper of :py:class:`monai.transforms.transforms.Orientation`.
+
+    This transform assumes the ``data`` dictionary has a field for the input
+    data's affine.  The field is created by either ``meta_key_format.format(key,
+    'affine')`` or ``meta_key_format.format(key, 'original_affine')``.
+
+    After reorientate the input array, this transform will store the current
+    affine in the ``data`` dictionary,
+    at the same time, if ``meta_key_format.format(key, 'original_affine')`` doesn't exist,
+    the field will be created and set to the affine before resampling.
     """
 
-    def __init__(self, keys, affine_key, axcodes, labels=None, output_key='orientation'):
+    def __init__(self, keys, axcodes=None, as_closest_canonical=False,
+                 labels=tuple(zip('LPI', 'RAS')), meta_key_format='{}.{}'):
         """
         Args:
-            affine_key (hashable): the key to the original affine.
-                The affine will be used to compute input data's orientation.
             axcodes (N elements sequence): for spatial ND input's orientation.
                 e.g. axcodes='RAS' represents 3D orientation:
                 (Left, Right), (Posterior, Anterior), (Inferior, Superior).
                 default orientation labels options are: 'L' and 'R' for the first dimension,
                 'P' and 'A' for the second, 'I' and 'S' for the third.
+            as_closest_canonical (boo): if True, load the image as closest to canonical axis format.
             labels : optional, None or sequence of (2,) sequences
                 (2,) sequences are labels for (beginning, end) of output axis.
+                Defaults to ``(('L', 'R'), ('P', 'A'), ('I', 'S'))``.
+            meta_key_format (str): key format to read/write affine matrices to the data dictionary.
 
         See Also:
             `nibabel.orientations.ornt2axcodes`.
         """
         MapTransform.__init__(self, keys)
-        self.affine_key = affine_key
-        self.orientation_transform = Orientation(axcodes=axcodes, labels=labels)
-        self.output_key = output_key
+        self.ornt_transform = Orientation(
+            axcodes=axcodes, as_closest_canonical=as_closest_canonical, labels=labels)
+        self.meta_key_format = meta_key_format
 
     def __call__(self, data):
         d = dict(data)
-        affine = d[self.affine_key]
-        original_ornt, new_ornt = None, None
         for key in self.keys:
-            d[key], original_ornt, new_ornt = self.orientation_transform(d[key], affine)
-        d[self.output_key] = {'original_ornt': original_ornt, 'current_ornt': new_ornt}
+            affine_key = self.meta_key_format.format(key, 'affine')
+            original_key = self.meta_key_format.format(key, 'original_affine')
+
+            affine = d.get(affine_key, None)
+            if affine is None:
+                affine = d.get(original_key, None)
+            d[key], affine_, new_affine = self.ornt_transform(d[key], affine)
+            if d.get(original_key, None) is None:
+                d[original_key] = affine_
+            d[affine_key] = new_affine
         return d
 
 
@@ -119,7 +171,8 @@ class LoadNiftid(MapTransform):
     together. If loading a list of files in one key, stack them together and
     add a new dimension as the first dimension, and use the meta data of the
     first image to represent the stacked result. Note that the affine transform
-    of all the stacked images should be same.
+    of all the stacked images should be same. The output metadata field will be created as
+    ``self.meta_key_format(key, metadata_key)``.
     """
 
     def __init__(self, keys, as_closest_canonical=False, dtype=np.float32,
@@ -144,13 +197,13 @@ def __call__(self, data):
         d = dict(data)
         for key in self.keys:
             data = self.loader(d[key])
-            assert isinstance(data, (tuple, list)), 'if data contains metadata, must be tuple or list.'
+            assert isinstance(data, (tuple, list)), 'loader must return a tuple or list.'
             d[key] = data[0]
-            assert isinstance(data[1], dict), 'metadata must be in dict format.'
-            for k in sorted(data[1].keys()):
+            assert isinstance(data[1], dict), 'metadata must be a dict.'
+            for k in sorted(data[1]):
                 key_to_add = self.meta_key_format.format(key, k)
-                if key_to_add in d and self.overwriting_keys is False:
-                    raise KeyError('meta data key is alreay existing.')
+                if key_to_add in d and not self.overwriting_keys:
+                    raise KeyError('meta data key {} already exists.'.format(key_to_add))
                 d[key_to_add] = data[1][k]
         return d
 
@@ -261,7 +314,7 @@ class Resized(MapTransform):
         mode (str): Points outside boundaries are filled according to given mode.
             Options are 'constant', 'edge', 'symmetric', 'reflect', 'wrap'.
         cval (float): Used with mode 'constant', the value outside image boundaries.
-        clip (bool): Wheter to clip range of output values after interpolation. Default: True.
+        clip (bool): Whether to clip range of output values after interpolation. Default: True.
         preserve_range (bool): Whether to keep original range of values. Default is True.
             If False, input is converted according to conventions of img_as_float. See
             https://scikit-image.org/docs/dev/user_guide/data_types.html.
@@ -503,7 +556,7 @@ def __init__(self, keys,
 
         See also:
             - :py:class:`monai.transforms.compose.MapTransform`
-            - :py:class:`RandAffineGrid` for the random affine paramters configurations.
+            - :py:class:`RandAffineGrid` for the random affine parameters configurations.
         """
         MapTransform.__init__(self, keys)
         default_mode = 'bilinear' if isinstance(mode, (tuple, list)) else mode
@@ -573,7 +626,7 @@ def __init__(self, keys,
                 whether to convert it back to numpy arrays.
             device (torch.device): device on which the tensor will be allocated.
         See also:
-            - :py:class:`RandAffineGrid` for the random affine paramters configurations.
+            - :py:class:`RandAffineGrid` for the random affine parameters configurations.
             - :py:class:`Affine` for the affine transformation parameters configurations.
         """
         MapTransform.__init__(self, keys)
@@ -647,7 +700,7 @@ def __init__(self, keys,
                 whether to convert it back to numpy arrays.
             device (torch.device): device on which the tensor will be allocated.
         See also:
-            - :py:class:`RandAffineGrid` for the random affine paramters configurations.
+            - :py:class:`RandAffineGrid` for the random affine parameters configurations.
             - :py:class:`Affine` for the affine transformation parameters configurations.
         """
         MapTransform.__init__(self, keys)
@@ -764,7 +817,7 @@ class Rotated(MapTransform):
         mode (str): Points outside boundary filled according to this mode. Options are
             'constant', 'nearest', 'reflect', 'wrap'. Default: 'constant'.
         cval (scalar): Values to fill outside boundary. Default: 0.
-        prefiter (bool): Apply spline_filter before interpolation. Default: True.
+        prefilter (bool): Apply spline_filter before interpolation. Default: True.
     """
 
     def __init__(self, keys, angle, spatial_axes=(0, 1), reshape=True, order=1,
@@ -797,7 +850,7 @@ class RandRotated(Randomizable, MapTransform):
         mode (str): Points outside boundary filled according to this mode. Options are
             'constant', 'nearest', 'reflect', 'wrap'. Default: 'constant'.
         cval (scalar): Value to fill outside boundary. Default: 0.
-        prefiter (bool): Apply spline_filter before interpolation. Default: True.
+        prefilter (bool): Apply spline_filter before interpolation. Default: True.
     """
 
     def __init__(self, keys, degrees, prob=0.1, spatial_axes=(0, 1), reshape=True, order=1,
diff --git a/monai/transforms/transforms.py b/monai/transforms/transforms.py
index 60aae8baf3..181f7e5cf2 100644
--- a/monai/transforms/transforms.py
+++ b/monai/transforms/transforms.py
@@ -13,6 +13,7 @@
 https://github.com/Project-MONAI/MONAI/wiki/MONAI_Design
 """
 
+import warnings
 import numpy as np
 import scipy.ndimage
 import nibabel as nib
@@ -21,11 +22,12 @@
 from skimage.transform import resize
 
 import monai
-from monai.data.utils import get_random_patch, get_valid_patch_size, correct_nifti_header_if_necessary
+from monai.data.utils import (get_random_patch, get_valid_patch_size, correct_nifti_header_if_necessary, zoom_affine,
+                              compute_shape_offset)
 from monai.networks.layers.simplelayers import GaussianFilter
 from monai.transforms.compose import Randomizable
 from monai.transforms.utils import (create_control_grid, create_grid, create_rotate, create_scale, create_shear,
-                                    create_translate, rescale_array)
+                                    create_translate, rescale_array, to_affine_nd)
 from monai.utils.misc import ensure_tuple
 
 export = monai.utils.export("monai.transforms")
@@ -37,51 +39,75 @@ class Spacing:
     Resample input image into the specified `pixdim`.
     """
 
-    def __init__(self, pixdim, keep_shape=False):
+    def __init__(self, pixdim, diagonal=False, mode='constant', cval=0, dtype=None):
         """
         Args:
             pixdim (sequence of floats): output voxel spacing.
-            keep_shape (bool): whether to maintain the original spatial shape
-                after resampling. Defaults to False.
+            diagonal (bool): whether to resample the input to have a diagonal affine matrix.
+                If True, the input data is resampled to the following affine::
+
+                    np.diag((pixdim_0, pixdim_1, ..., pixdim_n, 1))
+
+                This effectively resets the volume to the world coordinate system (RAS+ in nibabel).
+                The original orientation, rotation, shearing are not preserved.
+
+                If False, this transform preserves the axes orientation, orthogonal rotation and
+                translation components from the original affine. This option will not flip/swap axes
+                of the original data.
+            mode (`reflect|constant|nearest|mirror|wrap`):
+                The mode parameter determines how the input array is extended beyond its boundaries.
+            cval (scalar): Value to fill past edges of input if mode is "constant". Default is 0.0.
+            dtype (None or np.dtype): output array data type, defaults to None to use input data's dtype.
         """
-        self.pixdim = pixdim
-        self.keep_shape = keep_shape
-        self.original_pixdim = pixdim
+        self.pixdim = np.array(ensure_tuple(pixdim), dtype=np.float64)
+        self.diagonal = diagonal
+        self.mode = mode
+        self.cval = cval
+        self.dtype = dtype
 
-    def __call__(self, data_array, original_affine=None, original_pixdim=None, interp_order=1):
+    def __call__(self, data_array, original_affine=None, interp_order=3):
         """
         Args:
             data_array (ndarray): in shape (num_channels, H[, W, ...]).
-            original_affine (4x4 matrix): original affine.
-            original_pixdim (sequence of floats): original voxel spacing.
+            original_affine (4x4 matrix): original affine. Defaults to "eye(4)".
             interp_order (int): The order of the spline interpolation, default is 3.
                 The order has to be in the range 0-5.
                 https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.zoom.html
         Returns:
-            resampled array (in spacing: `self.pixdim`), original pixdim, current pixdim.
+            data_array (resampled into `self.pixdim`), original pixdim, current pixdim.
         """
-        if original_affine is None and original_pixdim is None:
-            raise ValueError('please provide either original_affine or original_pixdim.')
-        spatial_rank = data_array.ndim - 1
-        if original_affine is not None:
-            affine = np.array(original_affine, dtype=np.float64, copy=True)
-            if not affine.shape == (4, 4):
-                raise ValueError('`original_affine` must be 4 x 4.')
-            original_pixdim = np.sqrt(np.sum(np.square(affine[:spatial_rank, :spatial_rank]), 1))
-
-        inp_d = np.asarray(original_pixdim)[:spatial_rank]
-        if inp_d.size < spatial_rank:
-            inp_d = np.append(inp_d, [1.] * (inp_d.size - spatial_rank))
-        out_d = np.asarray(self.pixdim)[:spatial_rank]
-        if out_d.size < spatial_rank:
-            out_d = np.append(out_d, [1.] * (out_d.size - spatial_rank))
-
-        self.original_pixdim, self.pixdim = inp_d, out_d
-        scale = inp_d / out_d
-        if not np.isfinite(scale).all():
-            raise ValueError('Unknown pixdims: source {}, target {}'.format(inp_d, out_d))
-        zoom_ = monai.transforms.Zoom(scale, order=interp_order, mode='nearest', keep_size=self.keep_shape)
-        return zoom_(data_array), self.original_pixdim, self.pixdim
+        sr = data_array.ndim - 1
+        if sr <= 0:
+            raise ValueError('the array should have at least one spatial dimension.')
+        if original_affine is None:
+            # default to identity
+            original_affine = np.eye(sr + 1, dtype=np.float64)
+            affine = np.eye(sr + 1, dtype=np.float64)
+        else:
+            affine = to_affine_nd(sr, original_affine)
+        out_d = self.pixdim[:sr]
+        if out_d.size < sr:
+            out_d = np.append(out_d, [1.] * (out_d.size - sr))
+        if np.any(out_d <= 0):
+            raise ValueError('pixdim must be positive, got {}'.format(out_d))
+        # compute output affine, shape and offset
+        new_affine = zoom_affine(affine, out_d, diagonal=self.diagonal)
+        output_shape, offset = compute_shape_offset(data_array.shape[1:], affine, new_affine)
+        new_affine[:sr, -1] = offset[:sr]
+        transform = np.linalg.inv(affine) @ new_affine
+        # adapt to the actual rank
+        transform_ = to_affine_nd(sr, transform)
+        # resample
+        dtype = data_array.dtype if self.dtype is None else self.dtype
+        output_data = []
+        for data in data_array:
+            data_ = scipy.ndimage.affine_transform(
+                data.astype(dtype), matrix=transform_, output_shape=output_shape,
+                order=interp_order, mode=self.mode, cval=self.cval)
+            output_data.append(data_)
+        output_data = np.stack(output_data)
+        new_affine = to_affine_nd(original_affine, new_affine)
+        return output_data, original_affine, new_affine
 
 
 @export
@@ -90,7 +116,7 @@ class Orientation:
     Change the input image's orientation into the specified based on `axcodes`.
     """
 
-    def __init__(self, axcodes, labels=None):
+    def __init__(self, axcodes=None, as_closest_canonical=False, labels=tuple(zip('LPI', 'RAS'))):
         """
         Args:
             axcodes (N elements sequence): for spatial ND input's orientation.
@@ -98,42 +124,56 @@ def __init__(self, axcodes, labels=None):
                 (Left, Right), (Posterior, Anterior), (Inferior, Superior).
                 default orientation labels options are: 'L' and 'R' for the first dimension,
                 'P' and 'A' for the second, 'I' and 'S' for the third.
+            as_closest_canonical (boo): if True, load the image as closest to canonical axis format.
             labels : optional, None or sequence of (2,) sequences
                 (2,) sequences are labels for (beginning, end) of output axis.
+                Defaults to ``(('L', 'R'), ('P', 'A'), ('I', 'S'))``.
 
         See Also: `nibabel.orientations.ornt2axcodes`.
         """
+        if axcodes is None and not as_closest_canonical:
+            raise ValueError('provide either `axcodes` or `as_closest_canonical=True`.')
+        if axcodes is not None and as_closest_canonical:
+            warnings.warn('using as_closest_canonical=True, axcodes ignored.')
         self.axcodes = axcodes
+        self.as_closest_canonical = as_closest_canonical
         self.labels = labels
 
-    def __call__(self, data_array, original_affine=None, original_axcodes=None):
+    def __call__(self, data_array, original_affine=None):
         """
-        if `original_affine` is provided, the orientation is computed from the affine.
+        original orientation of `data_array` is defined by `original_affine`.
 
         Args:
             data_array (ndarray): in shape (num_channels, H[, W, ...]).
             original_affine (4x4 matrix): original affine.
-            original_axcodes (N elements sequence): for spatial ND input's orientation.
         Returns:
             data_array (reoriented in `self.axcodes`), original axcodes, current axcodes.
         """
-        if original_affine is None and original_axcodes is None:
-            raise ValueError('please provide either original_affine or original_axcodes.')
-        spatial_rank = len(data_array.shape) - 1
-        if original_affine is not None:
-            affine = np.array(original_affine, dtype=np.float64, copy=True)
-            if not affine.shape == (4, 4):
-                raise ValueError('`original_affine` must be 4 x 4.')
-            original_axcodes = nib.aff2axcodes(original_affine, labels=self.labels)
-        original_axcodes = original_axcodes[:spatial_rank]
-        self.axcodes = self.axcodes[:spatial_rank]
-        src = nib.orientations.axcodes2ornt(original_axcodes, labels=self.labels)
-        dst = nib.orientations.axcodes2ornt(self.axcodes)
-        spatial_ornt = nib.orientations.ornt_transform(src, dst)
-        spatial_ornt[:, 0] += 1  # skip channel dim
-        ornt = np.concatenate([np.array([[0, 1]]), spatial_ornt])
+        sr = data_array.ndim - 1
+        if sr <= 0:
+            raise ValueError('the array should have at least one spatial dimension.')
+        if original_affine is None:
+            original_affine = np.eye(sr + 1, dtype=np.float64)
+            affine = np.eye(sr + 1, dtype=np.float64)
+        else:
+            affine = to_affine_nd(sr, original_affine)
+        src = nib.io_orientation(affine)
+        if self.as_closest_canonical:
+            spatial_ornt = src
+        else:
+            dst = nib.orientations.axcodes2ornt(self.axcodes[:sr], labels=self.labels)
+            if len(dst) < sr:
+                raise ValueError('`self.axcodes` should have at least {0} elements'
+                                 ' given the data array is in spatial {0}D, got "{1}"'.format(sr, self.axcodes))
+            spatial_ornt = nib.orientations.ornt_transform(src, dst)
+        ornt = spatial_ornt.copy()
+        ornt[:, 0] += 1  # skip channel dim
+        ornt = np.concatenate([np.array([[0, 1]]), ornt])
+        shape = data_array.shape[1:]
         data_array = nib.orientations.apply_orientation(data_array, ornt)
-        return data_array, original_axcodes, self.axcodes
+        new_affine = affine @ nib.orientations.inv_ornt_aff(spatial_ornt, shape)
+        new_affine = to_affine_nd(original_affine, new_affine)
+        return data_array, original_affine, new_affine
 
 
 @export
@@ -149,14 +189,18 @@ def __init__(self, as_closest_canonical=False, image_only=False, dtype=np.float3
         """
         Args:
             as_closest_canonical (bool): if True, load the image as closest to canonical axis format.
-            image_only (bool): if True return only the image volume, other return image volume and header dict.
+            image_only (bool): if True return only the image volume, otherwise return image data array and header dict.
             dtype (np.dtype, optional): if not None convert the loaded image to this data type.
 
         Note:
-            The loaded image volume if `image_only` is True, or a tuple containing the volume and the Nifti
-            header in dict format otherwise.
-            header['original_affine'] stores the original affine loaded from `filename_or_obj`.
-            header['affine'] stores the affine after the optional `as_closest_canonical` transform.
+            The transform returns image data array if `image_only` is True,
+            or a tuple of two elements containing the data array, and the Nifti
+            header in a dict format otherwise.
+            if a dictionary header is returned:
+
+            - header['affine'] stores the affine of the image.
+            - header['original_affine'] will be additionally created to store the original affine,
+              if the current affine is different from that loaded from `filename_or_obj`.
         """
         self.as_closest_canonical = as_closest_canonical
         self.image_only = image_only
@@ -175,11 +219,11 @@ def __call__(self, filename):
             img = correct_nifti_header_if_necessary(img)
             header = dict(img.header)
             header['filename_or_obj'] = name
-            header['original_affine'] = img.affine
             header['affine'] = img.affine
             header['as_closest_canonical'] = self.as_closest_canonical
 
             if self.as_closest_canonical:
+                header['original_affine'] = img.affine
                 img = nib.as_closest_canonical(img)
                 header['affine'] = img.affine
 
@@ -292,8 +336,14 @@ def __init__(self, mean=0.0, std=0.1):
         self.mean = mean
         self.std = std
 
+        self._noise = None
+
+    def randomize(self, im_shape):
+        self._noise = self.R.normal(self.mean, self.R.uniform(0, self.std), size=im_shape)
+
     def __call__(self, img):
-        return img + self.R.normal(self.mean, self.R.uniform(0, self.std), size=img.shape)
+        self.randomize(img.shape)
+        return img + self._noise
 
 
 @export
@@ -334,7 +384,7 @@ class Resize:
         mode (str): Points outside boundaries are filled according to given mode.
             Options are 'constant', 'edge', 'symmetric', 'reflect', 'wrap'.
         cval (float): Used with mode 'constant', the value outside image boundaries.
-        clip (bool): Wheter to clip range of output values after interpolation. Default: True.
+        clip (bool): Whether to clip range of output values after interpolation. Default: True.
         preserve_range (bool): Whether to keep original range of values. Default is True.
             If False, input is converted according to conventions of img_as_float. See
             https://scikit-image.org/docs/dev/user_guide/data_types.html.
@@ -388,7 +438,7 @@ class Rotate:
         mode (str): Points outside boundary filled according to this mode. Options are
             'constant', 'nearest', 'reflect', 'wrap'. Default: 'constant'.
         cval (scalar): Values to fill outside boundary. Default: 0.
-        prefiter (bool): Apply spline_filter before interpolation. Default: True.
+        prefilter (bool): Apply spline_filter before interpolation. Default: True.
     """
 
     def __init__(self, angle, spatial_axes=(0, 1), reshape=True, order=1, mode='constant', cval=0, prefilter=True):
@@ -739,7 +789,7 @@ class RandRotate(Randomizable):
         mode (str): Points outside boundary filled according to this mode. Options are
             'constant', 'nearest', 'reflect', 'wrap'. Default: 'constant'.
         cval (scalar): Value to fill outside boundary. Default: 0.
-        prefiter (bool): Apply spline_filter before interpolation. Default: True.
+        prefilter (bool): Apply spline_filter before interpolation. Default: True.
     """
 
     def __init__(self, degrees, prob=0.1, spatial_axes=(0, 1), reshape=True, order=1,
@@ -823,8 +873,7 @@ class RandZoom(Randomizable):
     def __init__(self, prob=0.1, min_zoom=0.9, max_zoom=1.1, order=3,
                  mode='constant', cval=0, prefilter=True,
                  use_gpu=False, keep_size=False):
-        if hasattr(min_zoom, '__iter__') and \
-           hasattr(max_zoom, '__iter__'):
+        if hasattr(min_zoom, '__iter__') and hasattr(max_zoom, '__iter__'):
             assert len(min_zoom) == len(max_zoom), "min_zoom and max_zoom must have same length."
         self.min_zoom = min_zoom
         self.max_zoom = max_zoom
@@ -898,7 +947,7 @@ def __call__(self, spatial_size=None, grid=None):
             affine = affine @ create_scale(spatial_dims, self.scale_params)
         affine = torch.tensor(affine, device=self.device)
 
-        grid = torch.tensor(grid) if not torch.is_tensor(grid) else grid.clone().detach()
+        grid = torch.tensor(np.ascontiguousarray(grid)) if not torch.is_tensor(grid) else grid.detach().clone()
         if self.device:
             grid = grid.to(self.device)
         grid = (affine.float() @ grid.reshape((grid.shape[0], -1)).float()).reshape([-1] + list(grid.shape[1:]))
@@ -1013,7 +1062,7 @@ def __call__(self, spatial_size):
         self.randomize(control_grid.shape[1:])
         control_grid[:len(spatial_size)] += self.rand_mag * self.random_offset
         if self.as_tensor_output:
-            control_grid = torch.tensor(control_grid, device=self.device)
+            control_grid = torch.tensor(np.ascontiguousarray(control_grid), device=self.device)
         return control_grid
 
 
@@ -1041,8 +1090,8 @@ def __call__(self, img, grid, mode='bilinear'):
             mode ('nearest'|'bilinear'): interpolation order. Defaults to 'bilinear'.
         """
         if not torch.is_tensor(img):
-            img = torch.tensor(img)
-        grid = torch.tensor(grid) if not torch.is_tensor(grid) else grid.clone().detach()
+            img = torch.from_numpy(np.ascontiguousarray(img))
+        grid = torch.from_numpy(np.ascontiguousarray(grid)) if not torch.is_tensor(grid) else grid.detach().clone()
         if self.device:
             img = img.to(self.device)
             grid = grid.to(self.device)
@@ -1079,7 +1128,7 @@ def __init__(self,
                  as_tensor_output=False,
                  device=None):
         """
-        The affines are applied in rotate, shear, translate, scale order.
+        The affine transformations are applied in rotate, shear, translate, scale order.
 
         Args:
             rotate_params (float, list of floats): a rotation angle in radians,
@@ -1156,7 +1205,7 @@ def __init__(self,
             device (torch.device): device on which the tensor will be allocated.
 
         See also:
-            - :py:class:`RandAffineGrid` for the random affine paramters configurations.
+            - :py:class:`RandAffineGrid` for the random affine parameters configurations.
             - :py:class:`Affine` for the affine transformation parameters configurations.
         """
 
@@ -1235,7 +1284,7 @@ def __init__(self,
             device (torch.device): device on which the tensor will be allocated.
 
         See also:
-            - :py:class:`RandAffineGrid` for the random affine paramters configurations.
+            - :py:class:`RandAffineGrid` for the random affine parameters configurations.
             - :py:class:`Affine` for the affine transformation parameters configurations.
         """
         self.deform_grid = RandDeformGrid(spacing=spacing, magnitude_range=magnitude_range,
@@ -1317,7 +1366,7 @@ def __init__(self,
             device (torch.device): device on which the tensor will be allocated.
 
         See also:
-            - :py:class:`RandAffineGrid` for the random affine paramters configurations.
+            - :py:class:`RandAffineGrid` for the random affine parameters configurations.
             - :py:class:`Affine` for the affine transformation parameters configurations.
         """
         self.rand_affine_grid = RandAffineGrid(rotate_range, shear_range, translate_range, scale_range, True, device)
@@ -1360,7 +1409,7 @@ def __call__(self, img, spatial_size=None, mode=None):
         self.randomize(spatial_size)
         grid = create_grid(spatial_size)
         if self.do_transform:
-            grid = torch.tensor(grid).to(self.device)
+            grid = torch.tensor(np.ascontiguousarray(grid)).to(self.device)
             gaussian = GaussianFilter(3, self.sigma, 3., device=self.device)
             grid[:3] += gaussian(self.rand_offset[None])[0] * self.magnitude
             grid = self.rand_affine_grid(grid=grid)
diff --git a/monai/transforms/utils.py b/monai/transforms/utils.py
index f7a2f24501..e8b7c4c3b5 100644
--- a/monai/transforms/utils.py
+++ b/monai/transforms/utils.py
@@ -245,7 +245,7 @@ def create_grid(spatial_size, spacing=None, homogeneous=True, dtype=float):
     coords = np.asarray(np.meshgrid(*ranges, indexing='ij'), dtype=dtype)
     if not homogeneous:
         return coords
-    return np.concatenate([coords, np.ones_like(coords[0:1, ...])])
+    return np.concatenate([coords, np.ones_like(coords[:1])])
 
 
 def create_control_grid(spatial_shape, spacing, homogeneous=True, dtype=float):
@@ -362,3 +362,40 @@ def create_translate(spatial_dims, shift):
     for i, a in enumerate(shift[:spatial_dims]):
         affine[i, spatial_dims] = a
     return affine
+
+
+def to_affine_nd(r, affine):
+    """
+    Using elements from affine, to create a new affine matrix by
+    assigning the rotation/zoom/scaling matrix and the translation vector.
+
+    when ``r`` is an integer, output is an (r+1)x(r+1) matrix,
+    where the top left kxk elements are copied from ``affine``,
+    the last column of the output affine is copied from ``affine``'s last column.
+    `k` is determined by `min(r, len(affine) - 1)`.
+
+    when ``r`` is an affine matrix, the output has the same as ``r``,
+    the top left kxk elments are  copied from ``affine``,
+    the last column of the output affine is copied from ``affine``'s last column.
+    `k` is determined by `min(len(r) - 1, len(affine) - 1)`.
+
+
+    Args:
+        r (int or matrix): number of spatial dimensions or an output affine to be filled.
+        affine (matrix): 2D affine matrix
+    Returns:
+        a (r+1) x (r+1) matrix
+    """
+    affine = np.array(affine, dtype=np.float64)
+    if affine.ndim != 2:
+        raise ValueError('input affine must have two dimensions')
+    new_affine = np.array(r, dtype=np.float64, copy=True)
+    if new_affine.ndim == 0:
+        sr = new_affine.astype(int)
+        if not np.isfinite(sr) or sr < 0:
+            raise ValueError('r must be postive.')
+        new_affine = np.eye(sr + 1, dtype=np.float64)
+    d = min(len(new_affine) - 1, len(affine) - 1)
+    new_affine[:d, :d] = affine[:d, :d]
+    new_affine[:d, -1] = affine[:d, -1]
+    return new_affine
diff --git a/tests/test_load_spacing_orientation.py b/tests/test_load_spacing_orientation.py
new file mode 100644
index 0000000000..c160b6cef4
--- /dev/null
+++ b/tests/test_load_spacing_orientation.py
@@ -0,0 +1,109 @@
+# Copyright 2020 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import unittest
+
+import nibabel
+import numpy as np
+from nibabel.processing import resample_to_output
+from parameterized import parameterized
+
+from monai.transforms.composables import (AddChanneld, LoadNiftid, Orientationd, Spacingd)
+
+FILES = tuple(
+    os.path.join(os.path.dirname(__file__), 'testing_data', filename)
+    for filename in ('anatomical.nii', 'reoriented_anat_moved.nii'))
+
+
+class TestLoadSpacingOrientation(unittest.TestCase):
+
+    @parameterized.expand(FILES)
+    def test_load_spacingd(self, filename):
+        data = {'image': filename}
+        data_dict = LoadNiftid(keys='image')(data)
+        data_dict = AddChanneld(keys='image')(data_dict)
+        res_dict = Spacingd(keys='image', pixdim=(1, 2, 3), diagonal=True, mode='constant')(data_dict)
+        np.testing.assert_allclose(data_dict['image.affine'], res_dict['image.original_affine'])
+        anat = nibabel.Nifti1Image(data_dict['image'][0], data_dict['image.affine'])
+        ref = resample_to_output(anat, (1, 2, 3))
+        np.testing.assert_allclose(res_dict['image.affine'], ref.affine)
+        np.testing.assert_allclose(res_dict['image'].shape[1:], ref.shape)
+        np.testing.assert_allclose(ref.get_fdata(), res_dict['image'][0])
+
+    @parameterized.expand(FILES)
+    def test_load_spacingd_rotate(self, filename):
+        data = {'image': filename}
+        data_dict = LoadNiftid(keys='image')(data)
+        data_dict = AddChanneld(keys='image')(data_dict)
+        affine = data_dict['image.affine']
+        data_dict['image.affine'] = \
+            np.array([[0, 0, 1, 0], [0, 1, 0, 0], [-1, 0, 0, 0], [0, 0, 0, 1]]) @ affine
+        res_dict = Spacingd(keys='image', pixdim=(1, 2, 3), diagonal=True, mode='constant')(data_dict)
+        np.testing.assert_allclose(data_dict['image.affine'], res_dict['image.original_affine'])
+        anat = nibabel.Nifti1Image(data_dict['image'][0], data_dict['image.affine'])
+        ref = resample_to_output(anat, (1, 2, 3))
+        np.testing.assert_allclose(res_dict['image.affine'], ref.affine)
+        np.testing.assert_allclose(res_dict['image'].shape[1:], ref.shape)
+        np.testing.assert_allclose(ref.get_fdata(), res_dict['image'][0])
+
+    def test_load_spacingd_non_diag(self):
+        data = {'image': FILES[1]}
+        data_dict = LoadNiftid(keys='image')(data)
+        data_dict = AddChanneld(keys='image')(data_dict)
+        affine = data_dict['image.affine']
+        data_dict['image.affine'] = \
+            np.array([[0, 0, 1, 0], [0, 1, 0, 0], [-1, 0, 0, 0], [0, 0, 0, 1]]) @ affine
+        res_dict = Spacingd(keys='image', pixdim=(1, 2, 3), diagonal=False, mode='constant')(data_dict)
+        np.testing.assert_allclose(data_dict['image.affine'], res_dict['image.original_affine'])
+        np.testing.assert_allclose(
+            res_dict['image.affine'],
+            np.array([[0., 0., 3., -27.599409], [0., 2., 0., -47.977585], [-1., 0., 0., 35.297897], [0., 0., 0., 1.]]))
+
+    def test_load_spacingd_rotate_non_diag(self):
+        data = {'image': FILES[0]}
+        data_dict = LoadNiftid(keys='image')(data)
+        data_dict = AddChanneld(keys='image')(data_dict)
+        res_dict = Spacingd(keys='image', pixdim=(1, 2, 3), diagonal=False, mode='nearest')(data_dict)
+        np.testing.assert_allclose(data_dict['image.affine'], res_dict['image.original_affine'])
+        np.testing.assert_allclose(
+            res_dict['image.affine'],
+            np.array([[-1., 0., 0., 32.], [0., 2., 0., -40.], [0., 0., 3., -16.], [0., 0., 0., 1.]]))
+
+    def test_load_spacingd_rotate_non_diag_ornt(self):
+        data = {'image': FILES[0]}
+        data_dict = LoadNiftid(keys='image')(data)
+        data_dict = AddChanneld(keys='image')(data_dict)
+        res_dict = Spacingd(keys='image', pixdim=(1, 2, 3), diagonal=False, mode='nearest')(data_dict)
+        res_dict = Orientationd(keys='image', axcodes='LPI')(res_dict)
+        np.testing.assert_allclose(data_dict['image.affine'], res_dict['image.original_affine'])
+        np.testing.assert_allclose(
+            res_dict['image.affine'],
+            np.array([[-1., 0., 0., 32.], [0., -2., 0., 40.], [0., 0., -3., 32.], [0., 0., 0., 1.]]))
+
+    def test_load_spacingd_non_diag_ornt(self):
+        data = {'image': FILES[1]}
+        data_dict = LoadNiftid(keys='image')(data)
+        data_dict = AddChanneld(keys='image')(data_dict)
+        affine = data_dict['image.affine']
+        data_dict['image.affine'] = \
+            np.array([[0, 0, 1, 0], [0, 1, 0, 0], [-1, 0, 0, 0], [0, 0, 0, 1]]) @ affine
+        res_dict = Spacingd(keys='image', pixdim=(1, 2, 3), diagonal=False, mode='constant')(data_dict)
+        res_dict = Orientationd(keys='image', axcodes='LPI')(res_dict)
+        np.testing.assert_allclose(data_dict['image.affine'], res_dict['image.original_affine'])
+        np.testing.assert_allclose(
+            res_dict['image.affine'],
+            np.array([[-3., 0., 0., 56.4005909], [0., -2., 0., 52.02241516], [0., 0., -1., 35.29789734],
+                      [0., 0., 0., 1.]]))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/tests/test_nifti_header_revise.py b/tests/test_nifti_header_revise.py
new file mode 100644
index 0000000000..e28e30a60c
--- /dev/null
+++ b/tests/test_nifti_header_revise.py
@@ -0,0 +1,40 @@
+# Copyright 2020 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import nibabel as nib
+
+import numpy as np
+
+from monai.data.utils import rectify_header_sform_qform
+
+
+class TestRectifyHeaderSformQform(unittest.TestCase):
+
+    def test_revise_q(self):
+        img = nib.Nifti1Image(np.zeros((10, 10, 10)), np.eye(4))
+        img.header.set_zooms((0.1, 0.2, 0.3))
+        output = rectify_header_sform_qform(img)
+        expected = np.diag([0.1, 0.2, 0.3, 1.])
+        np.testing.assert_allclose(output.affine, expected)
+
+    def test_revise_both(self):
+        img = nib.Nifti1Image(np.zeros((10, 10, 10)), np.eye(4))
+        img.header.set_sform(np.diag([5, 3, 4, 1]))
+        img.header.set_qform(np.diag([2, 3, 4, 1]))
+        img.header.set_zooms((0.1, 0.2, 0.3))
+        output = rectify_header_sform_qform(img)
+        expected = np.diag([0.1, 0.2, 0.3, 1.])
+        np.testing.assert_allclose(output.affine, expected)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/tests/test_nifti_rw.py b/tests/test_nifti_rw.py
index d758380c70..9a557b3883 100644
--- a/tests/test_nifti_rw.py
+++ b/tests/test_nifti_rw.py
@@ -19,7 +19,7 @@
 from monai.data.nifti_reader import load_nifti
 from monai.data.nifti_writer import write_nifti
 
-from .utils import make_nifti_image
+from tests.utils import make_nifti_image
 
 TEST_IMAGE = np.zeros((1, 2, 3))
 TEST_AFFINE = np.array([[-5.3, 0., 0., 102.01], [0., 0.52, 2.17, -7.50], [-0., 1.98, -0.26, -23.12], [0., 0., 0., 1.]])
@@ -48,9 +48,9 @@ def test_orientation(self, array, affine, expected_shape, reader_param):
 
         # write test cases
         if header is not None:
-            write_nifti(data_array, header['affine'], test_image, header['original_affine'])
+            write_nifti(data_array, test_image, header['affine'], header['original_affine'])
         else:
-            write_nifti(data_array, affine, test_image)
+            write_nifti(data_array, test_image, affine)
         saved = nib.load(test_image)
         saved_affine = saved.affine
         saved_shape = saved.get_fdata().shape
diff --git a/tests/test_orientation.py b/tests/test_orientation.py
index 8cd1c55f79..cef045a0e9 100644
--- a/tests/test_orientation.py
+++ b/tests/test_orientation.py
@@ -11,27 +11,87 @@
 
 import unittest
 
+import nibabel as nib
 import numpy as np
 from parameterized import parameterized
 
 from monai.transforms.transforms import Orientation
+from monai.transforms.utils import create_rotate, create_translate
 
 TEST_CASES = [
     [{'axcodes': 'RAS'},
-     np.ones((2, 10, 15, 20)), {'original_axcodes': 'ALS'}, (2, 15, 10, 20)],
+     np.arange(12).reshape((2, 1, 2, 3)), {'original_affine': np.eye(4)},
+     np.arange(12).reshape((2, 1, 2, 3)), 'RAS'],
+    [{'axcodes': 'ALS'},
+     np.arange(12).reshape((2, 1, 2, 3)), {'original_affine': np.diag([-1, -1, 1, 1])},
+     np.array([[[[3, 4, 5]], [[0, 1, 2]]], [[[9, 10, 11]], [[6, 7, 8]]]]), 'ALS'],
+    [{'axcodes': 'RAS'},
+     np.arange(12).reshape((2, 1, 2, 3)), {'original_affine': np.diag([-1, -1, 1, 1])},
+     np.array([[[[3, 4, 5], [0, 1, 2]]], [[[9, 10, 11], [6, 7, 8]]]]), 'RAS'],
     [{'axcodes': 'AL'},
-     np.ones((2, 10, 15)), {'original_axcodes': 'AR'}, (2, 10, 15)],
+     np.arange(6).reshape((2, 1, 3)), {'original_affine': np.eye(3)},
+     np.array([[[0], [1], [2]], [[3], [4], [5]]]), 'AL'],
+    [{'axcodes': 'L'},
+     np.arange(6).reshape((2, 3)), {'original_affine': np.eye(2)},
+     np.array([[2, 1, 0], [5, 4, 3]]), 'L'],
+    [{'axcodes': 'L'},
+     np.arange(6).reshape((2, 3)), {'original_affine': np.eye(2)},
+     np.array([[2, 1, 0], [5, 4, 3]]), 'L'],
     [{'axcodes': 'L'},
-     np.ones((2, 10)), {'original_axcodes': 'R'}, (2, 10)],
+     np.arange(6).reshape((2, 3)), {'original_affine': np.diag([-1, 1])},
+     np.arange(6).reshape((2, 3)), 'L'],
+    [{'axcodes': 'LPS'},
+     np.arange(12).reshape((2, 1, 2, 3)), {
+         'original_affine':
+         create_translate(3, (10, 20, 30)) @
+         create_rotate(3, (np.pi / 2, np.pi / 2, np.pi / 4)) @ np.diag([-1, 1, 1, 1])},
+     np.array([[[[2, 5]], [[1, 4]], [[0, 3]]], [[[8, 11]], [[7, 10]], [[6, 9]]]]), 'LPS'],
+    [{'as_closest_canonical': True},
+     np.arange(12).reshape((2, 1, 2, 3)), {
+         'original_affine':
+         create_translate(3, (10, 20, 30)) @
+         create_rotate(3, (np.pi / 2, np.pi / 2, np.pi / 4)) @ np.diag([-1, 1, 1, 1])},
+     np.array([[[[0, 3]], [[1, 4]], [[2, 5]]], [[[6, 9]], [[7, 10]], [[8, 11]]]]), 'RAS'],
+    [{'as_closest_canonical': True},
+     np.arange(6).reshape((1, 2, 3)),
+     {'original_affine': create_translate(2, (10, 20)) @ create_rotate(2, (np.pi / 3)) @ np.diag([-1, -0.2, 1])},
+     np.array([[[3, 0], [4, 1], [5, 2]]]), 'RA'],
+    [{'axcodes': 'LP'},
+     np.arange(6).reshape((1, 2, 3)),
+     {'original_affine': create_translate(2, (10, 20)) @ create_rotate(2, (np.pi / 3)) @ np.diag([-1, -0.2, 1])},
+     np.array([[[2, 5], [1, 4], [0, 3]]]), 'LP'],
+    [{'axcodes': 'LPID', 'labels': tuple(zip('LPIC', 'RASD'))},
+     np.zeros((1, 2, 3, 4, 5)), {'original_affine': np.diag([-1, -0.2, -1, 1, 1])},
+     np.zeros((1, 2, 3, 4, 5)), 'LPID'],
+    [{'as_closest_canonical': True, 'labels': tuple(zip('LPIC', 'RASD'))},
+     np.zeros((1, 2, 3, 4, 5)), {'original_affine': np.diag([-1, -0.2, -1, 1, 1])},
+     np.zeros((1, 2, 3, 4, 5)), 'RASD'],
+]
+
+ILL_CASES = [
+    # no axcodes or as_cloest_canonical
+    [{}, np.arange(6).reshape((2, 3)), 'L'],
+    # too short axcodes
+    [{'axcodes': 'RA'}, np.arange(12).reshape((2, 1, 2, 3)), {'original_affine': np.eye(4)}],
 ]
 
 
 class TestOrientationCase(unittest.TestCase):
 
     @parameterized.expand(TEST_CASES)
-    def test_ornt(self, init_param, img, data_param, expected_shape):
-        res = Orientation(**init_param)(img, **data_param)
-        np.testing.assert_allclose(res[0].shape, expected_shape)
+    def test_ornt(self, init_param, img, data_param, expected_data, expected_code):
+        ornt = Orientation(**init_param)
+        res = ornt(img, **data_param)
+        np.testing.assert_allclose(res[0], expected_data)
+        original_affine = data_param['original_affine']
+        np.testing.assert_allclose(original_affine, res[1])
+        new_code = nib.orientations.aff2axcodes(res[2], labels=ornt.labels)
+        self.assertEqual(''.join(new_code), expected_code)
+
+    @parameterized.expand(ILL_CASES)
+    def test_bad_params(self, init_param, img, data_param):
+        with self.assertRaises(ValueError):
+            Orientation(**init_param)(img, **data_param)
 
 
 if __name__ == '__main__':
diff --git a/tests/test_orientationd.py b/tests/test_orientationd.py
index 999f31efe2..60b0a24069 100644
--- a/tests/test_orientationd.py
+++ b/tests/test_orientationd.py
@@ -11,6 +11,7 @@
 
 import unittest
 
+import nibabel as nib
 import numpy as np
 
 from monai.transforms.composables import Orientationd
@@ -19,36 +20,58 @@
 class TestOrientationdCase(unittest.TestCase):
 
     def test_orntd(self):
-        data = {'seg': np.ones((2, 1, 2, 3)), 'affine': np.eye(4)}
-        ornt = Orientationd(keys='seg', affine_key='affine', axcodes='RAS')
+        data = {'seg': np.ones((2, 1, 2, 3)), 'seg.affine': np.eye(4)}
+        ornt = Orientationd(keys='seg', axcodes='RAS')
         res = ornt(data)
         np.testing.assert_allclose(res['seg'].shape, (2, 1, 2, 3))
-        self.assertEqual(res['orientation']['original_ornt'], ('R', 'A', 'S'))
-        self.assertEqual(res['orientation']['current_ornt'], 'RAS')
+        code = nib.aff2axcodes(res['seg.affine'], ornt.ornt_transform.labels)
+        self.assertEqual(code, ('R', 'A', 'S'))
 
     def test_orntd_3d(self):
-        data = {'seg': np.ones((2, 1, 2, 3)), 'img': np.ones((2, 1, 2, 3)), 'affine': np.eye(4)}
-        ornt = Orientationd(keys=('img', 'seg'), affine_key='affine', axcodes='PLI')
+        data = {
+            'seg': np.ones((2, 1, 2, 3)), 'img': np.ones((2, 1, 2, 3)), 'seg.affine': np.eye(4), 'img.affine': np.eye(4)
+        }
+        ornt = Orientationd(keys=('img', 'seg'), axcodes='PLI')
         res = ornt(data)
         np.testing.assert_allclose(res['img'].shape, (2, 2, 1, 3))
-        self.assertEqual(res['orientation']['original_ornt'], ('R', 'A', 'S'))
-        self.assertEqual(res['orientation']['current_ornt'], 'PLI')
+        np.testing.assert_allclose(res['seg'].shape, (2, 2, 1, 3))
+        code = nib.aff2axcodes(res['seg.affine'], ornt.ornt_transform.labels)
+        self.assertEqual(code, ('P', 'L', 'I'))
+        code = nib.aff2axcodes(res['img.affine'], ornt.ornt_transform.labels)
+        self.assertEqual(code, ('P', 'L', 'I'))
 
     def test_orntd_2d(self):
-        data = {'seg': np.ones((2, 1, 3)), 'img': np.ones((2, 1, 3)), 'affine': np.eye(4)}
-        ornt = Orientationd(keys=('img', 'seg'), affine_key='affine', axcodes='PLI')
+        data = {'seg': np.ones((2, 1, 3)), 'img': np.ones((2, 1, 3)), 'seg.affine': np.eye(4), 'img.affine': np.eye(4)}
+        ornt = Orientationd(keys=('img', 'seg'), axcodes='PLI')
         res = ornt(data)
         np.testing.assert_allclose(res['img'].shape, (2, 3, 1))
-        self.assertEqual(res['orientation']['original_ornt'], ('R', 'A'))
-        self.assertEqual(res['orientation']['current_ornt'], 'PL')
+        code = nib.aff2axcodes(res['seg.affine'], ornt.ornt_transform.labels)
+        self.assertEqual(code, ('P', 'L', 'S'))
+        code = nib.aff2axcodes(res['img.affine'], ornt.ornt_transform.labels)
+        self.assertEqual(code, ('P', 'L', 'S'))
 
     def test_orntd_1d(self):
-        data = {'seg': np.ones((2, 3)), 'img': np.ones((2, 3)), 'affine': np.eye(4)}
-        ornt = Orientationd(keys=('img', 'seg'), affine_key='affine', axcodes='L')
+        data = {'seg': np.ones((2, 3)), 'img': np.ones((2, 3)), 'seg.affine': np.eye(4), 'img.affine': np.eye(4)}
+        ornt = Orientationd(keys=('img', 'seg'), axcodes='L')
         res = ornt(data)
         np.testing.assert_allclose(res['img'].shape, (2, 3))
-        self.assertEqual(res['orientation']['original_ornt'], ('R',))
-        self.assertEqual(res['orientation']['current_ornt'], 'L')
+        code = nib.aff2axcodes(res['seg.affine'], ornt.ornt_transform.labels)
+        self.assertEqual(code, ('L', 'A', 'S'))
+        code = nib.aff2axcodes(res['img.affine'], ornt.ornt_transform.labels)
+        self.assertEqual(code, ('L', 'A', 'S'))
+
+    def test_orntd_canonical(self):
+        data = {
+            'seg': np.ones((2, 1, 2, 3)), 'img': np.ones((2, 1, 2, 3)), 'seg.affine': np.eye(4), 'img.affine': np.eye(4)
+        }
+        ornt = Orientationd(keys=('img', 'seg'), as_closest_canonical=True)
+        res = ornt(data)
+        np.testing.assert_allclose(res['img'].shape, (2, 1, 2, 3))
+        np.testing.assert_allclose(res['seg'].shape, (2, 1, 2, 3))
+        code = nib.aff2axcodes(res['seg.affine'], ornt.ornt_transform.labels)
+        self.assertEqual(code, ('R', 'A', 'S'))
+        code = nib.aff2axcodes(res['img.affine'], ornt.ornt_transform.labels)
+        self.assertEqual(code, ('R', 'A', 'S'))
 
 
 if __name__ == '__main__':
diff --git a/tests/test_spacing.py b/tests/test_spacing.py
index ceaff9a9e6..378ad5598e 100644
--- a/tests/test_spacing.py
+++ b/tests/test_spacing.py
@@ -17,30 +17,108 @@
 from monai.transforms.transforms import Spacing
 
 TEST_CASES = [
-    [{'pixdim': (1.0, 2.0, 1.5)},
-     np.ones((2, 10, 15, 20)), {'original_pixdim': (0.5, 0.5, 1.0)}, (2, 5, 4, 13)],
-    [{'pixdim': (1.0, 2.0, 1.5), 'keep_shape': True},
-     np.ones((1, 2, 1, 2)), {'original_pixdim': (0.5, 0.5, 1.0)}, (1, 2, 1, 2)],
-    [{'pixdim': (1.0, 0.2, 1.5), 'keep_shape': False},
-     np.ones((1, 2, 1, 2)), {'original_affine': np.eye(4)}, (1, 2, 5, 1)],
-    [{'pixdim': (1.0, 2.0), 'keep_shape': True},
-     np.ones((3, 2, 2)), {'original_pixdim': (1.5, 0.5)}, (3, 2, 2)],
-    [{'pixdim': (1.0, 0.2), 'keep_shape': False},
-     np.ones((5, 2, 1)), {'original_pixdim': (1.5, 0.5)}, (5, 3, 2)],
-    [{'pixdim': (1.0,), 'keep_shape': False},
-     np.ones((1, 2)), {'original_pixdim': (1.5,), 'interp_order': 0}, (1, 3)],
+    [
+        {'pixdim': (2.0,)},
+        np.ones((1, 2)),  # data
+        {'original_affine': np.eye(4)},
+        np.array([[1., 0.]])
+    ],
+    [
+        {'pixdim': (1.0, 0.2, 1.5)},
+        np.ones((1, 2, 1, 2)),  # data
+        {'original_affine': np.eye(4)},
+        np.array([[[[1., 0.]], [[1., 0.]]]])
+    ],
+    [
+        {'pixdim': (1.0, 0.2, 1.5), 'diagonal': False},
+        np.ones((1, 2, 1, 2)),  # data
+        {
+            'original_affine': np.array([[2, 1, 0, 4], [-1, -3, 0, 5], [0, 0, 2., 5], [0, 0, 0, 1]],),
+        },
+        np.array([[[[0., 0., 0.]], [[0., 0., 0.]], [[0., 0., 0.]], [[0., 0., 0.]]]])
+    ],
+    [
+        {'pixdim': (3.0, 1.0)},
+        np.arange(24).reshape((2, 3, 4)),  # data
+        {'original_affine': np.diag([-3.0, 0.2, 1.5, 1])},
+        np.array([[[0, 0], [4, 0], [8, 0]], [[12, 0], [16, 0], [20, 0]]])
+    ],
+    [
+        {'pixdim': (3.0, 1.0)},
+        np.arange(24).reshape((2, 3, 4)),  # data
+        {},
+        np.array([[[0, 1, 2, 3], [0, 0, 0, 0]], [[12, 13, 14, 15], [0, 0, 0, 0]]])
+    ],
+    [
+        {'pixdim': (1.0, 1.0)},
+        np.arange(24).reshape((2, 3, 4)),  # data
+        {},
+        np.array([[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]], [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]]])
+    ],
+    [
+        {'pixdim': (4.0, 5.0, 6.0)},
+        np.arange(24).reshape((1, 2, 3, 4)),  # data
+        {'original_affine': np.array([[-4, 0, 0, 4], [0, 5, 0, -5], [0, 0, 6, -6], [0, 0, 0, 1]])},
+        np.arange(24).reshape((1, 2, 3, 4)),  # data
+    ],
+    [
+        {'pixdim': (4.0, 5.0, 6.0), 'diagonal': True},
+        np.arange(24).reshape((1, 2, 3, 4)),  # data
+        {'original_affine': np.array([[-4, 0, 0, 4], [0, 5, 0, 0], [0, 0, 6, 0], [0, 0, 0, 1]])},
+        np.array([[[[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]],
+                   [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]]])
+    ],
+    [
+        {'pixdim': (4.0, 5.0, 6.0), 'mode': 'nearest', 'diagonal': True},
+        np.arange(24).reshape((1, 2, 3, 4)),  # data
+        {'original_affine': np.array([[-4, 0, 0, -4], [0, 5, 0, 0], [0, 0, 6, 0], [0, 0, 0, 1]])},
+        np.array([[[[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]],
+                   [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]]])
+    ],
+    [
+        {'pixdim': (4.0, 5.0, 6.0), 'mode': 'nearest', 'diagonal': True},
+        np.arange(24).reshape((1, 2, 3, 4)),  # data
+        {'original_affine': np.array([[-4, 0, 0, -4], [0, 5, 0, 0], [0, 0, 6, 0], [0, 0, 0, 1]]), 'interp_order': 0},
+        np.array([[[[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]],
+                   [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]]])
+    ],
+    [
+        {'pixdim': (2.0, 5.0, 6.0), 'mode': 'constant', 'diagonal': True},
+        np.arange(24).reshape((1, 4, 6)),  # data
+        {'original_affine': np.array([[-4, 0, 0, -4], [0, 5, 0, 0], [0, 0, 6, 0], [0, 0, 0, 1]]), 'interp_order': 0},
+        np.array([[[18, 19, 20, 21, 22, 23], [18, 19, 20, 21, 22, 23], [12, 13, 14, 15, 16, 17],
+                   [12, 13, 14, 15, 16, 17], [6, 7, 8, 9, 10, 11], [6, 7, 8, 9, 10, 11], [0, 1, 2, 3, 4, 5]]])
+    ],
+    [
+        {'pixdim': (5., 3., 6.), 'mode': 'constant', 'diagonal': True, 'dtype': np.float32},
+        np.arange(24).reshape((1, 4, 6)),  # data
+        {'original_affine': np.array([[-4, 0, 0, 0], [0, 5, 0, 0], [0, 0, 6, 0], [0, 0, 0, 1]]), 'interp_order': 0},
+        np.array([[[18., 19., 19., 20., 20., 21., 22., 22., 23., 0.], [12., 13., 13., 14., 14., 15., 16., 16., 17., 0.],
+                   [6., 7., 7., 8., 8., 9., 10., 10., 11., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],)
+    ],
+    [
+        {'pixdim': (5., 3., 6.), 'mode': 'constant', 'diagonal': True, 'dtype': np.float32},
+        np.arange(24).reshape((1, 4, 6)),  # data
+        {'original_affine': np.array([[-4, 0, 0, 0], [0, 5, 0, 0], [0, 0, 6, 0], [0, 0, 0, 1]]), 'interp_order': 2},
+        np.array(
+            [[[18., 18.492683, 19.22439, 19.80683, 20.398048, 21., 21.570732, 22.243902, 22.943415, 0.],
+              [10.392858, 10.88554, 11.617248, 12.199686, 12.790906, 13.392858, 13.963589, 14.63676, 15.336272, 0.],
+              [2.142857, 2.63554, 3.3672473, 3.9496865, 4.540906, 5.142857, 5.7135887, 6.3867598, 7.086272, 0.],
+              [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],)
+    ],
 ]
 
 
 class TestSpacingCase(unittest.TestCase):
 
     @parameterized.expand(TEST_CASES)
-    def test_spacing(self, init_param, img, data_param, expected_shape):
+    def test_spacing(self, init_param, img, data_param, expected_output):
         res = Spacing(**init_param)(img, **data_param)
-        np.testing.assert_allclose(res[0].shape, expected_shape)
-        if 'original_pixdim' in data_param:
-            np.testing.assert_allclose(res[1], data_param['original_pixdim'])
-        np.testing.assert_allclose(res[2], init_param['pixdim'])
+        np.testing.assert_allclose(res[0], expected_output, atol=1e-6)
+        if 'original_affine' in data_param:
+            np.testing.assert_allclose(res[1], data_param['original_affine'])
+        np.testing.assert_allclose(init_param['pixdim'],
+                                   np.sqrt(np.sum(np.square(res[2]), axis=0))[:len(init_param['pixdim'])])
 
 
 if __name__ == '__main__':
diff --git a/tests/test_spacingd.py b/tests/test_spacingd.py
index 3ee0b66ae1..3e75c534f1 100644
--- a/tests/test_spacingd.py
+++ b/tests/test_spacingd.py
@@ -19,44 +19,55 @@
 class TestSpacingDCase(unittest.TestCase):
 
     def test_spacingd_3d(self):
-        data = {'image': np.ones((2, 10, 15, 20)), 'affine': np.eye(4)}
-        spacing = Spacingd(keys='image', affine_key='affine', pixdim=(1, 2, 1.4))
+        data = {'image': np.ones((2, 10, 15, 20)), 'image.affine': np.eye(4)}
+        spacing = Spacingd(keys='image', pixdim=(1, 2, 1.4))
         res = spacing(data)
-        np.testing.assert_allclose(res['image'].shape, (2, 10, 8, 14))
-        np.testing.assert_allclose(res['spacing']['current_pixdim'], (1, 2, 1.4))
-        np.testing.assert_allclose(res['spacing']['original_pixdim'], (1, 1, 1))
+        self.assertEqual(('image', 'image.affine', 'image.original_affine'), tuple(sorted(res)))
+        np.testing.assert_allclose(res['image'].shape, (2, 10, 8, 15))
+        np.testing.assert_allclose(res['image.affine'], np.diag([1, 2, 1.4, 1.]))
+        np.testing.assert_allclose(res['image.original_affine'], np.eye(4))
 
     def test_spacingd_2d(self):
-        data = {'image': np.ones((2, 10, 20)), 'affine': np.eye(4)}
-        spacing = Spacingd(keys='image', affine_key='affine', pixdim=(1, 2, 1.4))
+        data = {'image': np.ones((2, 10, 20))}
+        spacing = Spacingd(keys='image', pixdim=(1, 2, 1.4))
         res = spacing(data)
-        np.testing.assert_allclose(res['image'].shape, (2, 10, 10))
-        np.testing.assert_allclose(res['spacing']['current_pixdim'], (1, 2))
-        np.testing.assert_allclose(res['spacing']['original_pixdim'], (1, 1))
+        self.assertEqual(('image', 'image.affine', 'image.original_affine'), tuple(sorted(res)))
+        np.testing.assert_allclose(res['image'].shape, (2, 10, 11))
+        np.testing.assert_allclose(res['image.affine'], np.diag((1, 2, 1)))
+        np.testing.assert_allclose(res['image.original_affine'], np.eye(3))
 
     def test_spacingd_1d(self):
-        data = {'image': np.ones((2, 10)), 'affine': np.eye(4)}
-        spacing = Spacingd(keys='image', affine_key='affine', pixdim=(0.2,))
+        data = {'image': np.arange(20).reshape((2, 10)), 'image.original_affine': np.diag((3, 2, 1, 1))}
+        spacing = Spacingd(keys='image', pixdim=(0.2,))
         res = spacing(data)
-        np.testing.assert_allclose(res['image'].shape, (2, 50))
-        np.testing.assert_allclose(res['spacing']['current_pixdim'], (0.2,))
-        np.testing.assert_allclose(res['spacing']['original_pixdim'], (1,))
+        self.assertEqual(('image', 'image.affine', 'image.original_affine'), tuple(sorted(res)))
+        np.testing.assert_allclose(res['image'].shape, (2, 136))
+        np.testing.assert_allclose(res['image.affine'], np.diag((0.2, 2, 1, 1)))
+        np.testing.assert_allclose(res['image.original_affine'], np.diag((3, 2, 1, 1)))
 
     def test_interp_all(self):
-        data = {'image': np.ones((2, 10)), 'seg': np.ones((2, 10)), 'affine': np.eye(4)}
-        spacing = Spacingd(keys=('image', 'seg'), affine_key='affine', interp_order=0, pixdim=(0.2,))
+        data = {'image': np.arange(20).reshape((2, 10)), 'seg': np.ones((2, 10)),
+                'image.affine': np.eye(4), 'seg.affine': np.eye(4)}
+        spacing = Spacingd(keys=('image', 'seg'), interp_order=0, pixdim=(0.2,))
         res = spacing(data)
-        np.testing.assert_allclose(res['image'].shape, (2, 50))
-        np.testing.assert_allclose(res['spacing']['current_pixdim'], (0.2,))
-        np.testing.assert_allclose(res['spacing']['original_pixdim'], (1,))
+        self.assertEqual(('image', 'image.affine', 'image.original_affine',
+                          'seg', 'seg.affine', 'seg.original_affine'),
+                         tuple(sorted(res)))
+        np.testing.assert_allclose(res['image'].shape, (2, 46))
+        np.testing.assert_allclose(res['image.affine'], np.diag((0.2, 1, 1, 1)))
+        np.testing.assert_allclose(res['image.original_affine'], np.eye(4))
 
     def test_interp_sep(self):
-        data = {'image': np.ones((2, 10)), 'seg': np.ones((2, 10)), 'affine': np.eye(4)}
-        spacing = Spacingd(keys=('image', 'seg'), affine_key='affine', interp_order=(2, 0), pixdim=(0.2,))
+        data = {'image': np.ones((2, 10)), 'seg': np.ones((2, 10)),
+                'image.affine': np.eye(4)}
+        spacing = Spacingd(keys=('image', 'seg'), interp_order=(2, 0), pixdim=(0.2,))
         res = spacing(data)
-        np.testing.assert_allclose(res['image'].shape, (2, 50))
-        np.testing.assert_allclose(res['spacing']['current_pixdim'], (0.2,))
-        np.testing.assert_allclose(res['spacing']['original_pixdim'], (1,))
+        self.assertEqual(('image', 'image.affine', 'image.original_affine',
+                          'seg', 'seg.affine', 'seg.original_affine'),
+                         tuple(sorted(res)))
+        np.testing.assert_allclose(res['image'].shape, (2, 46))
+        np.testing.assert_allclose(res['image.affine'], np.diag((0.2, 1, 1, 1)))
+        np.testing.assert_allclose(res['image.original_affine'], np.eye(4))
 
 
 if __name__ == '__main__':
diff --git a/tests/test_zoom_affine.py b/tests/test_zoom_affine.py
new file mode 100644
index 0000000000..194abf3ccb
--- /dev/null
+++ b/tests/test_zoom_affine.py
@@ -0,0 +1,85 @@
+# Copyright 2020 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import nibabel as nib
+import numpy as np
+from parameterized import parameterized
+
+from monai.data.utils import zoom_affine
+
+VALID_CASES = [
+    (
+        np.array([[2, 1, 4], [-1, -3, 5], [0, 0, 1]], ),
+        (10, 20, 30),
+        np.array([[8.94427191, -8.94427191, 0], [-4.47213595, -17.88854382, 0], [0., 0., 1.]], ),
+    ),
+    (
+        np.array([[1, 0, 0, 4], [0, 2, 0, 5], [0, 0, 3, 6], [0, 0, 0, 1]], ),
+        (10, 20, 30),
+        np.array([[10, 0, 0, 0], [0, 20, 0, 0], [0, 0, 30, 0], [0, 0, 0, 1]], ),
+    ),
+    (
+        np.array([[1, 0, 0, 4], [0, 2, 0, 5], [0, 0, 3, 6], [0, 0, 0, 1]], ),
+        (10, 20),
+        np.array([[10, 0, 0, 0], [0, 20, 0, 0], [0, 0, 3, 0], [0, 0, 0, 1]], ),
+    ),
+    (
+        np.array([[1, 0, 0, 4], [0, 2, 0, 5], [0, 0, 3, 6], [0, 0, 0, 1]], ),
+        (10,),
+        np.array([[10, 0, 0, 0], [0, 2, 0, 0], [0, 0, 3, 0], [0, 0, 0, 1]], ),
+    ),
+    (
+        [[1, 0, 10], [0, 1, 20], [0, 0, 1]]
+        @ ([[0, -1, 0], [1, 0, 0], [0, 0, 1]] @ np.array([[2, 0.3, 0], [0, 3, 0], [0, 0, 1]])),
+        (4, 5, 6),
+        ([[0, -1, 0], [1, 0, 0], [0, 0, 1]] @ np.array([[4, 0, 0], [0, 5, 0], [0, 0, 1]])),
+    ),
+]
+
+DIAGONAL_CASES = [
+    (
+        np.array([[-1, 0, 0, 4], [0, 2, 0, 5], [0, 0, 3, 6], [0, 0, 0, 1]], ),
+        (10, 20, 30),
+        np.array([[10, 0, 0, 0], [0, 20, 0, 0], [0, 0, 30, 0], [0, 0, 0, 1]], ),
+    ),
+    (
+        np.array([[2, 1, 4], [-1, -3, 5], [0, 0, 1]], ),
+        (10, 20, 30),
+        np.array([[10, 0, 0], [0, 20, 0], [0., 0., 1.]], ),
+    ),
+    (  # test default scale from affine
+        np.array([[2, 1, 4], [-1, -3, 5], [0, 0, 1]], ),
+        (10, ),
+        np.array([[10, 0, 0], [0, 3.162278, 0], [0., 0., 1.]], ),
+    ),
+]
+
+
+class TestZoomAffine(unittest.TestCase):
+
+    @parameterized.expand(VALID_CASES)
+    def test_correct(self, affine, scale, expected):
+        output = zoom_affine(affine, scale, diagonal=False)
+        ornt_affine = nib.orientations.ornt2axcodes(nib.orientations.io_orientation(output))
+        ornt_output = nib.orientations.ornt2axcodes(nib.orientations.io_orientation(affine))
+        np.testing.assert_array_equal(ornt_affine, ornt_output)
+        np.testing.assert_allclose(output, expected, rtol=1e-6, atol=1e-6)
+
+    @parameterized.expand(DIAGONAL_CASES)
+    def test_diagonal(self, affine, scale, expected):
+        output = zoom_affine(affine, scale, diagonal=True)
+        np.testing.assert_allclose(output, expected, rtol=1e-6, atol=1e-6)
+
+
+if __name__ == '__main__':
+    unittest.main()
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index 0000000000..2f2411d115
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