4DetectNodules.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Detect Nodules from Kaggle Dataset\n",
    "\n",
    "## Summary\n",
    "* load and process kaggle dataset\n",
    "* Generate prediction masks with trained unet\n",
    "* Reduce false positives with trained CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:64: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation=\"relu\", padding=\"same\")`\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:66: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation=\"relu\", padding=\"same\")`\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:69: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), activation=\"relu\", padding=\"same\")`\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:71: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), activation=\"relu\", padding=\"same\")`\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:74: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), activation=\"relu\", padding=\"same\")`\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:76: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), activation=\"relu\", padding=\"same\")`\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:79: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), activation=\"relu\", padding=\"same\")`\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:81: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), activation=\"relu\", padding=\"same\")`\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:84: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), activation=\"relu\", padding=\"same\")`\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:86: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), activation=\"relu\", padding=\"same\")`\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:88: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\legacy\\layers.py:458: UserWarning: The `Merge` layer is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n",
      "  name=name)\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:90: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), activation=\"relu\", padding=\"same\")`\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:91: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), activation=\"relu\", padding=\"same\")`\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:93: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:95: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), activation=\"relu\", padding=\"same\")`\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:96: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), activation=\"relu\", padding=\"same\")`\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:98: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:100: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), activation=\"relu\", padding=\"same\")`\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:101: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), activation=\"relu\", padding=\"same\")`\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:103: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:105: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation=\"relu\", padding=\"same\")`\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:106: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation=\"relu\", padding=\"same\")`\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:107: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(1, (1, 1), activation=\"sigmoid\")`\n",
      "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:109: UserWarning: Update your `Model` call to the Keras 2 API: `Model(outputs=Tensor(\"co..., inputs=Tensor(\"in...)`\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "### EDIT HERE ###\n",
    "\n",
    "unetweightspath=\"modelweights/unet-weights-improvement.hdf5\"\n",
    "truenoduleweightspath=\"modelweights/truenodule-cnn-weights-improvement.hdf5\"\n",
    "INPUT_FOLDER = 'stage1/' #path to kaggle stage1 dataset\n",
    "datafolder=\"processeddata/\"\n",
    "\n",
    "####################\n",
    "import numpy as np # linear algebra\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import dicom\n",
    "import os\n",
    "import scipy.ndimage\n",
    "import time\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "import h5py\n",
    "from sklearn.cluster import KMeans\n",
    "from skimage import measure, morphology\n",
    "import cell_magic_wand as cmw\n",
    "import numpy as np\n",
    "import csv\n",
    "import random\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier as RF\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from scipy.ndimage.measurements import center_of_mass, label\n",
    "from skimage.measure import regionprops\n",
    "\n",
    "import keras\n",
    "from keras.models import Sequential,load_model,Model\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D, SpatialDropout2D\n",
    "from keras.layers import Input, merge, UpSampling2D, BatchNormalization\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from keras import backend as K\n",
    "from keras.utils import to_categorical\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras import backend as K\n",
    "from keras.optimizers import Adam\n",
    "# Some constants \n",
    "\n",
    "patients = os.listdir(INPUT_FOLDER)\n",
    "#patients=patients.sort()\n",
    "K.set_image_dim_ordering('th') \n",
    "\n",
    "#Code sourced from https://www.kaggle.com/c/data-science-bowl-2017#tutorial\n",
    "smooth = 1.0\n",
    "width = 32\n",
    "\n",
    "def dice_coef(y_true, y_pred):\n",
    "    y_true_f = K.flatten(y_true)\n",
    "    y_pred_f = K.flatten(y_pred)\n",
    "    intersection = K.sum(y_true_f * y_pred_f)\n",
    "    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)\n",
    "def dice_coef_loss(y_true, y_pred):\n",
    "    return -dice_coef(y_true, y_pred)\n",
    "\n",
    "def unet_model():\n",
    "    inputs = Input((1, 512, 512))\n",
    "    conv1 = Conv2D(width, 3, 3, activation='relu', border_mode='same')(inputs)\n",
    "    conv1 = BatchNormalization(axis = 1)(conv1)\n",
    "    conv1 = Conv2D(width, 3, 3, activation='relu', border_mode='same')(conv1)\n",
    "    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n",
    "\n",
    "    conv2 = Conv2D(width*2, 3, 3, activation='relu', border_mode='same')(pool1)\n",
    "    conv2 = BatchNormalization(axis = 1)(conv2)\n",
    "    conv2 = Conv2D(width*2, 3, 3, activation='relu', border_mode='same')(conv2)\n",
    "    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n",
    "\n",
    "    conv3 = Conv2D(width*4, 3, 3, activation='relu', border_mode='same')(pool2)\n",
    "    conv3 = BatchNormalization(axis = 1)(conv3)\n",
    "    conv3 = Conv2D(width*4, 3, 3, activation='relu', border_mode='same')(conv3)\n",
    "    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)\n",
    "\n",
    "    conv4 = Conv2D(width*8, 3, 3, activation='relu', border_mode='same')(pool3)\n",
    "    conv4 = BatchNormalization(axis = 1)(conv4)\n",
    "    conv4 = Conv2D(width*8, 3, 3, activation='relu', border_mode='same')(conv4)\n",
    "    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)\n",
    "\n",
    "    conv5 = Conv2D(width*16, 3, 3, activation='relu', border_mode='same')(pool4)\n",
    "    conv5 = BatchNormalization(axis = 1)(conv5)\n",
    "    conv5 = Conv2D(width*16, 3, 3, activation='relu', border_mode='same')(conv5)\n",
    "\n",
    "    up6 = merge([UpSampling2D(size=(2, 2))(conv5), conv4], mode='concat', concat_axis=1)\n",
    "    conv6 = SpatialDropout2D(0.35)(up6)\n",
    "    conv6 = Conv2D(width*8, 3, 3, activation='relu', border_mode='same')(conv6)\n",
    "    conv6 = Conv2D(width*8, 3, 3, activation='relu', border_mode='same')(conv6)\n",
    "\n",
    "    up7 = merge([UpSampling2D(size=(2, 2))(conv6), conv3], mode='concat', concat_axis=1)\n",
    "    conv7 = SpatialDropout2D(0.35)(up7)\n",
    "    conv7 = Conv2D(width*4, 3, 3, activation='relu', border_mode='same')(conv7)\n",
    "    conv7 = Conv2D(width*4, 3, 3, activation='relu', border_mode='same')(conv7)\n",
    "\n",
    "    up8 = merge([UpSampling2D(size=(2, 2))(conv7), conv2], mode='concat', concat_axis=1)\n",
    "    conv8 = SpatialDropout2D(0.35)(up8)\n",
    "    conv8 = Conv2D(width*2, 3, 3, activation='relu', border_mode='same')(conv8)\n",
    "    conv8 = Conv2D(width*2, 3, 3, activation='relu', border_mode='same')(conv8)\n",
    "\n",
    "    up9 = merge([UpSampling2D(size=(2, 2))(conv8), conv1], mode='concat', concat_axis=1)\n",
    "    conv9 = SpatialDropout2D(0.35)(up9)\n",
    "    conv9 = Conv2D(width, 3, 3, activation='relu', border_mode='same')(conv9)\n",
    "    conv9 = Conv2D(width, 3, 3, activation='relu', border_mode='same')(conv9)\n",
    "    conv10 = Conv2D(1, 1, 1, activation='sigmoid')(conv9)\n",
    "\n",
    "    model = Model(input=inputs, output=conv10)\n",
    "    model.compile(optimizer=Adam(lr=1e-5), loss=dice_coef_loss, metrics=[dice_coef])\n",
    "    return model\n",
    "\n",
    "unet_model=unet_model()\n",
    "unet_model.load_weights(unetweightspath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#classify as nodule or non-nodule\n",
    "input_shape=(1,512,512)\n",
    "num_classes=2\n",
    "model = Sequential()\n",
    "model.add(Conv2D(8, kernel_size=(3, 3),\n",
    "                 activation='relu',\n",
    "                 input_shape=input_shape))\n",
    "model.add(Conv2D(16, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.compile(loss=keras.losses.binary_crossentropy,\n",
    "              optimizer=Adam(lr=1e-5),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "\n",
    "model.load_weights(truenoduleweightspath)\n",
    "#os.environ[\"PATH\"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'\n",
    "#plot_model(model, to_file=\"CNNdiagram.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load the scans in given folder path\n",
    "def load_scan(path):\n",
    "    # code sourced from https://www.kaggle.com/gzuidhof/full-preprocessing-tutorial\n",
    "    slices = [dicom.read_file(path + '/' + s, force=True) for s in os.listdir(path) if s.endswith('.dcm')]\n",
    "    slices.sort(key = lambda x: float(x.ImagePositionPatient[2]), reverse=True)\n",
    "    try:\n",
    "        slice_thickness = np.abs(slices[0].ImagePositionPatient[2] - slices[1].ImagePositionPatient[2])\n",
    "    except:\n",
    "        slice_thickness = np.abs(slices[0].SliceLocation - slices[1].SliceLocation)\n",
    "        \n",
    "    for s in slices:\n",
    "        s.SliceThickness = slice_thickness\n",
    "        \n",
    "    return slices\n",
    "\n",
    "def get_pixels_hu(slices):\n",
    "    #code sourced from https://www.kaggle.com/gzuidhof/full-preprocessing-tutorial\n",
    "    image = np.stack([s.pixel_array for s in slices])\n",
    "    # Convert to int16 (from sometimes int16), \n",
    "    # should be possible as values should always be low enough (<32k)\n",
    "    image = image.astype(np.int16)\n",
    "\n",
    "    # Set outside-of-scan pixels to 0\n",
    "    # The intercept is usually -1024, so air is approximately 0\n",
    "    image[image == -2000] = 0\n",
    "    \n",
    "    # Convert to Hounsfield units (HU)\n",
    "    for slice_number in range(len(slices)):\n",
    "        \n",
    "        intercept = slices[slice_number].RescaleIntercept\n",
    "        slope = slices[slice_number].RescaleSlope\n",
    "        \n",
    "        if slope != 1:\n",
    "            image[slice_number] = slope * image[slice_number].astype(np.float64)\n",
    "            image[slice_number] = image[slice_number].astype(np.int16)\n",
    "            \n",
    "        image[slice_number] += np.int16(intercept)\n",
    "    \n",
    "    return np.array(image, dtype=np.int16)\n",
    "\n",
    "def processimage(img):\n",
    "    #function sourced from https://www.kaggle.com/c/data-science-bowl-2017#tutorial\n",
    "    #Standardize the pixel values\n",
    "    mean = np.mean(img)\n",
    "    std = np.std(img)\n",
    "    img = img-mean\n",
    "    img = img/std\n",
    "    #plt.hist(img.flatten(),bins=200)\n",
    "    #plt.show()\n",
    "    #print(thresh_img[366][280:450])\n",
    "    middle = img[100:400,100:400] \n",
    "    mean = np.mean(middle)  \n",
    "    max = np.max(img)\n",
    "    min = np.min(img)\n",
    "    #move the underflow bins\n",
    "    img[img==max]=mean\n",
    "    img[img==min]=mean\n",
    "    kmeans = KMeans(n_clusters=2).fit(np.reshape(middle,[np.prod(middle.shape),1]))\n",
    "    centers = sorted(kmeans.cluster_centers_.flatten())\n",
    "    threshold = np.mean(centers)\n",
    "    thresh_img = np.where(img<threshold,1.0,0.0)  # threshold the image\n",
    "    eroded = morphology.erosion(thresh_img,np.ones([4,4]))\n",
    "    dilation = morphology.dilation(eroded,np.ones([10,10]))\n",
    "    labels = measure.label(dilation)\n",
    "    label_vals = np.unique(labels)\n",
    "    #plt.imshow(labels)\n",
    "    #plt.show()\n",
    "    labels = measure.label(dilation)\n",
    "    label_vals = np.unique(labels)\n",
    "    regions = measure.regionprops(labels)\n",
    "    good_labels = []\n",
    "    for prop in regions:\n",
    "        B = prop.bbox\n",
    "        if B[2]-B[0]<475 and B[3]-B[1]<475 and B[0]>40 and B[2]<472:\n",
    "            good_labels.append(prop.label)\n",
    "    mask = np.ndarray([512,512],dtype=np.int8)\n",
    "    mask[:] = 0\n",
    "    #\n",
    "    #  The mask here is the mask for the lungs--not the nodes\n",
    "    #  After just the lungs are left, we do another large dilation\n",
    "    #  in order to fill in and out the lung mask \n",
    "    #\n",
    "    for N in good_labels:\n",
    "        mask = mask + np.where(labels==N,1,0)\n",
    "    mask = morphology.dilation(mask,np.ones([10,10])) # one last dilation\n",
    "    return mask*img\n",
    "\n",
    "def processimagenomask(img):\n",
    "    #Standardize the pixel values\n",
    "    mean = np.mean(img)\n",
    "    std = np.std(img)\n",
    "    img = img-mean\n",
    "    img = img/std\n",
    "    #plt.hist(img.flatten(),bins=200)\n",
    "    #plt.show()\n",
    "    #print(thresh_img[366][280:450])\n",
    "    middle = img[100:400,100:400] \n",
    "    mean = np.mean(middle)  \n",
    "    max = np.max(img)\n",
    "    min = np.min(img)\n",
    "    #move the underflow bins\n",
    "    img[img==max]=mean\n",
    "    img[img==min]=mean\n",
    "    return img\n",
    "\n",
    "def processimagefromfile(ppix):\n",
    "    processpix=np.ndarray([ppix.shape[0],1,512,512])\n",
    "    for i in range(ppix.shape[0]):\n",
    "        processpix[i,0]=processimage(ppix[i])\n",
    "    return processpix\n",
    "\n",
    "#predict mask from images\n",
    "def predictmask(images):\n",
    "    num_test=images.shape[0]\n",
    "    imgs_mask_test = np.ndarray([num_test,1,512,512],dtype=np.float32)\n",
    "    for i in range(num_test):\n",
    "        imgs_mask_test[i] = unet_model.predict([images[i:i+1]], verbose=0)[0]\n",
    "    return imgs_mask_test\n",
    "\n",
    "#find number of slices where a nodule is detected\n",
    "def getnoduleindex(imgs_mask_test):\n",
    "    masksum=[np.sum(maskslice[0]) for maskslice in imgs_mask_test]\n",
    "    return [i for i in range(len(masksum)) if masksum[i]>5]\n",
    "\n",
    "def trueindicies(processed_pix, noduleindex):\n",
    "    noduleimgs=[processed_pix[ind] for ind in noduleindex]\n",
    "    noduleimgs=np.array(noduleimgs)\n",
    "    predictions=model.predict(noduleimgs)\n",
    "    predictions=predictions[:len(predictions),1]\n",
    "    predictions[predictions>0.5]=True\n",
    "    predictions[predictions<0.5]=False\n",
    "    trueindicies=[ind for i,ind in enumerate(noduleindex) if predictions[i]==1]\n",
    "    return trueindicies\n",
    "\n",
    "def thresholdnodules(noduleindices,mask):\n",
    "    nodulearea=[]\n",
    "    for ind in trueindicies:\n",
    "        nodulearea.append(np.sum(mask[ind]))\n",
    "    return nodulearea\n",
    "\n",
    "def largestnodulecoordinates(mask):\n",
    "    #mask=nodulemasks[indx,0][0]\n",
    "    mask[mask>0.5]=1\n",
    "    mask[mask<0.5]=0\n",
    "    labeled_array,nf=label(mask)\n",
    "    areasinslice=[]\n",
    "    if nf>1:\n",
    "        for n in range(nf):\n",
    "            lab=np.array(labeled_array)\n",
    "            lab[lab!=(n+1)]=0\n",
    "            lab[lab==(n+1)]=1\n",
    "            areasinslice.append(np.sum(lab))\n",
    "        nlargest=areasinslice.index(max(areasinslice))\n",
    "        labeled_array[labeled_array!=(nlargest+1)]=0\n",
    "        com=center_of_mass(labeled_array)\n",
    "    else:\n",
    "        com=center_of_mass(mask)\n",
    "    return [int(com[0]),int(com[1])]\n",
    "\n",
    "def largestnodulearea(mask,table,i):\n",
    "    #mask=nodulemasks[indx,0][0]\n",
    "    mask[mask>0.5]=1\n",
    "    mask[mask<0.5]=0\n",
    "    labeled_array,nf=label(mask)\n",
    "    areasinslice=[]\n",
    "    if nf>1:\n",
    "        for n in range(nf):\n",
    "            lab=np.array(labeled_array)\n",
    "            lab[lab!=(n+1)]=0\n",
    "            lab[lab==(n+1)]=1\n",
    "            areasinslice.append(np.sum(lab))\n",
    "        #nlargest=areasinslice.index(max(areasinslice))\n",
    "        #labeled_array[labeled_array!=(nlargest+1)]=0\n",
    "        return max(areasinslice)\n",
    "    else:\n",
    "        return table[\"Area\"][i]\n",
    "\n",
    "def crop_nodule(coord,image):\n",
    "    dim=32\n",
    "    return image[coord[0]-dim:coord[0]+dim,coord[1]-dim:coord[1]+dim]\n",
    "#output: 64x64 images of the nodules with malignancy labels from the patient\n",
    "\n",
    "def largestnoduleproperties(mask):\n",
    "    mask[mask>0.5]=1\n",
    "    mask[mask<0.5]=0\n",
    "    mask=mask.astype(np.int8)\n",
    "    labeled_array,nf=label(mask)\n",
    "    areasinslice=[]\n",
    "    if nf>1:\n",
    "        for n in range(nf):\n",
    "            lab=np.array(labeled_array)\n",
    "            lab[lab!=(n+1)]=0\n",
    "            lab[lab==(n+1)]=1\n",
    "            areasinslice.append(np.sum(lab))\n",
    "        nlargest=areasinslice.index(max(areasinslice))\n",
    "        labeled_array[labeled_array!=(nlargest+1)]=0\n",
    "        noduleprops=regionprops(labeled_array)\n",
    "    else:\n",
    "        noduleprops=regionprops(mask)\n",
    "    area=noduleprops[0].area\n",
    "    eccentricity=noduleprops[0].eccentricity\n",
    "    diam=noduleprops[0].equivalent_diameter\n",
    "    diammajor=noduleprops[0].major_axis_length\n",
    "    spiculation=noduleprops[0].solidity\n",
    "    return area, eccentricity, diam, diammajor, spiculation\n",
    "\n",
    "def generatefeaturetable(nodulemasks):\n",
    "    meannoduleHU=[]\n",
    "    nodulecount=[]\n",
    "    largestarealist=[]\n",
    "    eccentricitylist=[]\n",
    "    diamlist=[]\n",
    "    diammajorlist=[]\n",
    "    spiculationlist=[]\n",
    "\n",
    "    for i in range(nodulemasks.shape[0]):\n",
    "        mask=nodulemasks[i,0]\n",
    "        mask[mask>0.5]=1\n",
    "        mask[mask<0.5]=0\n",
    "        meannoduleHU.append(np.sum(noduleimages[i,0]*mask)/np.sum(mask))\n",
    "        labeled_array,features=label(mask)\n",
    "        nodulecount.append(features)\n",
    "        area, eccentricity, diam, diammajor, spiculation = largestnoduleproperties(nodulemasks[i,0])\n",
    "        largestarealist.append(area)\n",
    "        eccentricitylist.append(eccentricity)\n",
    "        diamlist.append(diam)\n",
    "        diammajorlist.append(diammajor)\n",
    "        spiculationlist.append(spiculation)\n",
    "    table=pd.DataFrame({\"Patient\":sample,\"NoduleIndex\":noduleindicies,\"Area\":area,\"MeanHU\":meannoduleHU, \"LargestNoduleArea\":largestarealist,\n",
    "                    \"Eccentricity\":eccentricitylist, \"Diameter\":diamlist, \"DiameterMajor\":diammajorlist, \"Spiculation\":spiculationlist})\n",
    "    return table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Processing patient# 1518 ETA: 1.6766352182999515 hrs\n",
      "Processing patient# 1519 ETA: 1.6640672508136443 hrs\n",
      "Processing patient# 1520 ETA: 1.6443906538188457 hrs\n",
      "Processing patient# 1521 ETA: 1.6265579485526016 hrs\n",
      "Processing patient# 1522 ETA: 1.6064969870503643 hrs\n",
      "Processing patient# 1523 ETA: 1.5878849256592276 hrs\n",
      "Processing patient# 1524 ETA: 1.5670396502469308 hrs\n",
      "Processing patient# 1526 ETA: 1.5448135244235013 hrs\n",
      "Processing patient# 1527 ETA: 1.5007179911218453 hrs\n",
      "Processing patient# 1528 ETA: 1.4779989854163593 hrs\n",
      "Processing patient# 1529 ETA: 1.455040394503777 hrs\n",
      "Processing patient# 1530 ETA: 1.4348017759756608 hrs\n",
      "Processing patient# 1531 ETA: 1.412959918495631 hrs\n",
      "Processing patient# 1532 ETA: 1.3895584875255462 hrs\n",
      "Processing patient# 1534 ETA: 1.3681563406241992 hrs\n",
      "Processing patient# 1535 ETA: 1.32147174191119 hrs\n",
      "Processing patient# 1536 ETA: 1.2993491290633876 hrs\n",
      "Processing patient# 1537 ETA: 1.2765257389907825 hrs\n",
      "Processing patient# 1538 ETA: 1.2545430914596223 hrs\n",
      "Processing patient# 1539 ETA: 1.2330312886358439 hrs\n",
      "Processing patient# 1540 ETA: 1.2134439415684322 hrs\n",
      "Processing patient# 1541 ETA: 1.1903008280926082 hrs\n",
      "Processing patient# 1542 ETA: 1.167653547794189 hrs\n",
      "Processing patient# 1543 ETA: 1.1457156837554199 hrs\n",
      "Processing patient# 1544 ETA: 1.1241135936051372 hrs\n",
      "Processing patient# 1545 ETA: 1.102846443816275 hrs\n",
      "Processing patient# 1546 ETA: 1.080637407885313 hrs\n",
      "Processing patient# 1547 ETA: 1.0583609296631968 hrs\n",
      "Processing patient# 1549 ETA: 1.0358784646273498 hrs\n",
      "Processing patient# 1550 ETA: 0.9894958379524097 hrs\n",
      "Processing patient# 1551 ETA: 0.9702932011284904 hrs\n",
      "Processing patient# 1552 ETA: 0.9487030035562142 hrs\n",
      "Processing patient# 1553 ETA: 0.927032014874493 hrs\n",
      "Processing patient# 1555 ETA: 0.9052574532595503 hrs\n",
      "Processing patient# 1556 ETA: 0.8583338576951499 hrs\n",
      "Processing patient# 1557 ETA: 0.8364724836388824 hrs\n",
      "Processing patient# 1558 ETA: 0.8143605039141544 hrs\n",
      "Processing patient# 1559 ETA: 0.7939183002461303 hrs\n",
      "Processing patient# 1560 ETA: 0.7714008516864275 hrs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing patient# 1561 ETA: 0.7496390868227617 hrs\n",
      "Processing patient# 1562 ETA: 0.7277910622262829 hrs\n",
      "Processing patient# 1563 ETA: 0.70534063768343 hrs\n",
      "Processing patient# 1564 ETA: 0.6836017272867927 hrs\n",
      "Processing patient# 1566 ETA: 0.6618160449642033 hrs\n",
      "Processing patient# 1568 ETA: 0.6160384095808674 hrs\n",
      "Processing patient# 1570 ETA: 0.5702290712717382 hrs\n",
      "Processing patient# 1571 ETA: 0.5249562931103638 hrs\n",
      "Processing patient# 1572 ETA: 0.5033148116076286 hrs\n",
      "Processing patient# 1573 ETA: 0.48112341017214527 hrs\n",
      "Processing patient# 1574 ETA: 0.4594317513380257 hrs\n",
      "Processing patient# 1575 ETA: 0.43778636997364123 hrs\n",
      "Processing patient# 1576 ETA: 0.415779529424036 hrs\n",
      "Processing patient# 1577 ETA: 0.3949588738670405 hrs\n",
      "Processing patient# 1578 ETA: 0.37282882327230255 hrs\n",
      "Processing patient# 1580 ETA: 0.3515907668564129 hrs\n",
      "Processing patient# 1581 ETA: 0.30684781963013846 hrs\n",
      "Processing patient# 1582 ETA: 0.28561606805205886 hrs\n",
      "Processing patient# 1583 ETA: 0.26367890904528596 hrs\n",
      "Processing patient# 1584 ETA: 0.241728710744323 hrs\n",
      "Processing patient# 1585 ETA: 0.2196190606033997 hrs\n",
      "Processing patient# 1587 ETA: 0.19766932272849366 hrs\n",
      "Processing patient# 1588 ETA: 0.1533620524758064 hrs\n",
      "Processing patient# 1589 ETA: 0.1314311540085944 hrs\n",
      "Processing patient# 1590 ETA: 0.10954693088589212 hrs\n",
      "Processing patient# 1591 ETA: 0.08776483735769842 hrs\n",
      "Processing patient# 1592 ETA: 0.06588565785057654 hrs\n",
      "Processing patient# 1594 ETA: 0.04392167075191941 hrs\n",
      "31161.49111223221\n"
     ]
    }
   ],
   "source": [
    "start_time=time.time()\n",
    "\n",
    "elapsed_time=0\n",
    "totaltime=94000\n",
    "thresh=-500 #lower HU threshold for nodule segmentation\n",
    "noduleimages=np.ndarray([5000,1,512,512],dtype=np.float32)\n",
    "nodulemasks=np.ndarray([5000,1,512,512],dtype=np.float32)\n",
    "sample=[]\n",
    "area=[]\n",
    "noduleindicies=[]\n",
    "index=0\n",
    "start=1\n",
    "end=400\n",
    "for i in range(len(patients)):\n",
    "    print(\"Processing patient#\",i,\"ETA:\",(totaltime-elapsed_time)/3600,\"hrs\")\n",
    "    if (i-1)/400-np.floor((i-1)/400)==0:\n",
    "        noduleimages=noduleimages[:index]\n",
    "        nodulemasks=nodulemasks[:index]\n",
    "        table=generatefeaturetable(nodulemasks)\n",
    "        print(\"Saving data for patients\"+str(start)+\"-\"+str(end))\n",
    "        np.save(datafolder+\"DSBNoduleImages\"+str(start)+\"-\"+str(end)+\".npy\",noduleimages)\n",
    "        np.save(datafolder+\"DSBNoduleMasks\"+str(start)+\"-\"+str(end)+\".npy\",nodulemasks)\n",
    "        table.to_csv(datafolder+\"DSBNoduleFeatures\"+str(start)+\"-\"+str(end)+\".csv\")\n",
    "        del noduleimages, nodulemasks\n",
    "        noduleimages=np.ndarray([5000,1,512,512],dtype=np.float32)\n",
    "        nodulemasks=np.ndarray([5000,1,512,512],dtype=np.float32)\n",
    "        sample=[]\n",
    "        area=[]\n",
    "        noduleindicies=[]\n",
    "        index=0  \n",
    "    patient_scan=load_scan(INPUT_FOLDER+patients[i])\n",
    "    patient_pix=get_pixels_hu(patient_scan)\n",
    "    processed_pix = processimagefromfile(patient_pix)\n",
    "    mask = predictmask(processed_pix)\n",
    "    noduleindex = getnoduleindex(mask)\n",
    "    trueinds=trueindicies(processed_pix,noduleindex)\n",
    "\n",
    "    for ind in trueinds:\n",
    "        noduleimages[index,0]=patient_pix[ind]\n",
    "        nodulemasks[index,0]=mask[ind]\n",
    "        sample.append(patients[i])\n",
    "        area.append(np.sum(mask[ind]))\n",
    "        noduleindicies.append(ind)\n",
    "        index+=1\n",
    "\n",
    "    elapsed_time=time.time()-start_time\n",
    "    totaltime=elapsed_time/(i-start+1)*(end-start)\n",
    "\n",
    "\n",
    "\n",
    "print(elapsed_time)"
   ]
  }
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