diff --git a/colabs/monai/3d_brain_tumor_segmentation.ipynb b/colabs/monai/3d_brain_tumor_segmentation.ipynb
index b0a6149f..a6d2f057 100644
--- a/colabs/monai/3d_brain_tumor_segmentation.ipynb
+++ b/colabs/monai/3d_brain_tumor_segmentation.ipynb
@@ -6,12 +6,12 @@
    "source": [
     "# Brain tumor 3D segmentation with MONAI and Weights & Biases\n",
     "\n",
-    "This tutorial shows how to construct a training workflow of multi-labels segmentation task using [MONAI](https://github.com/Project-MONAI/MONAI) and use experiment tracking and data visualization features of [Weights & Biases](https://wandb.ai/site). The tutorial contains the following features:\n",
+    "This tutorial shows how to construct a training workflow of multi-labels 3D brain tumor segmentation task using [MONAI](https://github.com/Project-MONAI/MONAI) and use experiment tracking and data visualization features of [Weights & Biases](https://wandb.ai/site). The tutorial contains the following features:\n",
     "\n",
     "1. Initialize a Weights & Biases run and synchrozize all configs associated with the run for reproducibility.\n",
     "2. MONAI transform API:\n",
     "    1. MONAI Transforms for dictionary format data.\n",
-    "    2. How to define a new transform according to MONAI transform API.\n",
+    "    2. How to define a new transform according to MONAI `transforms` API.\n",
     "    3. How to randomly adjust intensity for data augmentation.\n",
     "3. Data Loading and Visualization:\n",
     "    1. Load Nifti image with metadata, load a list of images and stack them.\n",
@@ -29,7 +29,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Setup and Installation\n",
+    "## 🌴 Setup and Installation\n",
     "\n",
     "First, let us install the latest version of both MONAI and Weights and Biases."
    ]
@@ -102,14 +102,37 @@
     "wandb.login()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🌳 Initialize a W&B Run\n",
+    "\n",
+    "We will start a new W&B run to start tracking our experiment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wandb.init(project=\"monai-brain-tumor-segmentation\", job_type=\"test\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use of proper config system is a recommended best practice for reproducible machine learning. We can track the hyperparameters for every experiment using W&B."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "wandb.init(project=\"monai-brain-tumor-segmentation\", job_type=\"test\")\n",
-    "\n",
     "config = wandb.config\n",
     "config.seed = 0\n",
     "config.roi_size = [224, 224, 144]\n",
@@ -132,16 +155,38 @@
     "config.max_prediction_images_visualized = 20"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We would also need to set the random seed for modules to enable or disable deterministic training."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "os.makedirs(config.dataset_dir, exist_ok=True)\n",
-    "os.makedirs(config.checkpoint_dir, exist_ok=True)\n",
+    "set_determinism(seed=config.seed)\n",
     "\n",
-    "set_determinism(seed=config.seed)"
+    "# Create directories\n",
+    "os.makedirs(config.dataset_dir, exist_ok=True)\n",
+    "os.makedirs(config.checkpoint_dir, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💿 Data Loading and Transformation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we use the `monai.transforms` API to create a custom transform that converts the multi-classes labels into multi-labels segmentation task in one-hot format."
    ]
   },
   {
@@ -159,6 +204,8 @@
     "    The possible classes are TC (Tumor core), WT (Whole tumor)\n",
     "    and ET (Enhancing tumor).\n",
     "\n",
+    "    Reference: https://github.com/Project-MONAI/tutorials/blob/main/3d_segmentation/brats_segmentation_3d.ipynb\n",
+    "\n",
     "    \"\"\"\n",
     "\n",
     "    def __call__(self, data):\n",
@@ -179,6 +226,13 @@
     "        return d"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we set up transforms for training and validation datasets respectively."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -226,6 +280,17 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 🍁 The Dataset\n",
+    "\n",
+    "The dataset that we will use for this experiment comes from http://medicaldecathlon.com/. We will use Multimodal multisite MRI data (FLAIR, T1w, T1gd, T2w) to segment Gliomas, necrotic/active tumour, and oedema. The dataset consists of 750 4D volumes (484 Training + 266 Testing).\n",
+    "\n",
+    "We will use the `DecathlonDataset` to automatically download and extract the dataset. It inherits MONAI `CacheDataset` which enables us to set `cache_num=N` to cache `N` items for training and use the default args to cache all the items for validation, depending on your memory size."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -252,6 +317,20 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note:** Instead of applying the `train_transform` to the `train_dataset`, we have applied `val_transform` to both the training and validation datasets. This is because, before training, we would be visualizing samples from both the splits of the dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 📸 Visualizing the Dataset"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,