ImageCLEF2019Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "import os\n",
    "import pandas as pd\n",
    "from keras.utils import np_utils\n",
    "\n",
    "import SimpleITK as sitk\n",
    "import os\n",
    "import numpy as np \n",
    "import scipy.ndimage\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy.random import rand\n",
    "from skimage import measure, morphology\n",
    "import cv2\n",
    "from scipy.ndimage import zoom\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define paths\n",
    "base_path = os.path.abspath(\"./\") # Your root directory\n",
    "dataset_path = os.path.join(base_path, \"dataset\") # Your dataset folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Filename</th>\n",
       "      <th>md_Disability</th>\n",
       "      <th>md_Relapse</th>\n",
       "      <th>md_SymptomsOfTB</th>\n",
       "      <th>md_Comorbidity</th>\n",
       "      <th>md_Bacillary</th>\n",
       "      <th>md_DrugResistance</th>\n",
       "      <th>md_HigherEducation</th>\n",
       "      <th>md_ExPrisoner</th>\n",
       "      <th>md_Alcoholic</th>\n",
       "      <th>md_Smoking</th>\n",
       "      <th>md_Severity</th>\n",
       "      <th>SVR_Severity</th>\n",
       "      <th>CTR_LeftLungAffected</th>\n",
       "      <th>CTR_RightLungAffected</th>\n",
       "      <th>CTR_LungCapacityDecrease</th>\n",
       "      <th>CTR_Calcification</th>\n",
       "      <th>CTR_Pleurisy</th>\n",
       "      <th>CTR_Caverns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CTR_TRN_001.nii.gz</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>LOW</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CTR_TRN_002.nii.gz</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>LOW</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CTR_TRN_003.nii.gz</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>LOW</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CTR_TRN_004.nii.gz</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>LOW</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CTR_TRN_005.nii.gz</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "             Filename  md_Disability  md_Relapse  md_SymptomsOfTB  \\\n",
       "0  CTR_TRN_001.nii.gz              0           0                0   \n",
       "1  CTR_TRN_002.nii.gz              0           0                0   \n",
       "2  CTR_TRN_003.nii.gz              0           0                1   \n",
       "3  CTR_TRN_004.nii.gz              0           0                0   \n",
       "4  CTR_TRN_005.nii.gz              0           0                0   \n",
       "\n",
       "   md_Comorbidity  md_Bacillary  md_DrugResistance  md_HigherEducation  \\\n",
       "0               1             0                  0                   0   \n",
       "1               1             1                  3                   0   \n",
       "2               1             0                  0                   0   \n",
       "3               0             1                  2                   0   \n",
       "4               1             1                  2                   0   \n",
       "\n",
       "   md_ExPrisoner  md_Alcoholic  md_Smoking  md_Severity SVR_Severity  \\\n",
       "0              0             1           1            4          LOW   \n",
       "1              0             0           1            4          LOW   \n",
       "2              0             0           0            5          LOW   \n",
       "3              0             0           1            4          LOW   \n",
       "4              1             0           1            3         HIGH   \n",
       "\n",
       "   CTR_LeftLungAffected  CTR_RightLungAffected  CTR_LungCapacityDecrease  \\\n",
       "0                     1                      0                         0   \n",
       "1                     0                      1                         0   \n",
       "2                     1                      1                         0   \n",
       "3                     1                      0                         0   \n",
       "4                     1                      0                         0   \n",
       "\n",
       "   CTR_Calcification  CTR_Pleurisy  CTR_Caverns  \n",
       "0                  0             0            0  \n",
       "1                  0             0            0  \n",
       "2                  1             0            0  \n",
       "3                  0             0            0  \n",
       "4                  0             0            0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = pd.read_csv(os.path.join(dataset_path, '8ba42c65-534a-4cdf-b983-e7fcaaaa3002_TrainingSet_metaData.csv'))\n",
    "dt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['TestSet_Masks', '8ba42c65-534a-4cdf-b983-e7fcaaaa3002_TrainingSet_metaData.csv', '5aa39c27-0c20-4330-b28e-9af8a5369033_TestSet_metaData.csv', '975bbbb8-62f9-4d79-a165-50dfad603f7b_README.txt', 'TestSet', 'TrainingSet_1_of_2', 'TrainingSet_2_of_2', 'y_train_clef.npy', 'TrainingSet_Masks']\n",
      "/home/hasib/uniformizing-3D/dataset/TrainingSet_1_of_2/\n",
      "['CTR_TRN_001.nii.gz', 'CTR_TRN_002.nii.gz', 'CTR_TRN_003.nii.gz', 'CTR_TRN_004.nii.gz', 'CTR_TRN_005.nii.gz', 'CTR_TRN_006.nii.gz', 'CTR_TRN_007.nii.gz', 'CTR_TRN_008.nii.gz', 'CTR_TRN_009.nii.gz', 'CTR_TRN_010.nii.gz']\n"
     ]
    }
   ],
   "source": [
    "# Raw data path\n",
    "INPUT_FOLDER = 'TrainingSet_1_of_2/'  \n",
    "training_data_raw = os.path.join(dataset_path, INPUT_FOLDER)\n",
    "\n",
    "# Check inside target folder\n",
    "print(os.listdir(dataset_path))\n",
    "print(training_data_raw)\n",
    "\n",
    "patients = sorted(os.listdir(training_data_raw))\n",
    "print(patients[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resize 2D slices\n",
    "w, h = 128, 128\n",
    "def rs_img(img):\n",
    "    '''W and H is 128 now\n",
    "    '''\n",
    "    img = np.transpose(img)\n",
    "    flatten = [cv2.resize(img[:,:,i], (w, h), interpolation=cv2.INTER_CUBIC) for i in range(img.shape[-1])]\n",
    "    img = np.array(np.dstack(flatten)) \n",
    "    return img\n",
    "\n",
    "\n",
    "# Subset slice selection (SSS)\n",
    "def change_depth_sss(img):\n",
    "    \n",
    "    factor = 16\n",
    "    img_start = img[:,:,:factor]\n",
    "    \n",
    "    mid = int(img.shape[-1]/2)\n",
    "    img_middle = img[:,:,mid-factor:mid+factor]\n",
    "    \n",
    "    img_end = img[:,:,-factor:]\n",
    "    img = np.concatenate((img_start, img_middle, img_end), axis=2)\n",
    "    return img\n",
    "\n",
    "\n",
    "# Even slice selection (ESS)\n",
    "def change_depth_ess(img):\n",
    "    \n",
    "    '''ESS depth size is 32 as min depth size is 47, so we cannot evenly select 64 slices!'''\n",
    "\n",
    "    # Ignore volumes which have depth less than 64\n",
    "    target_depth = 64\n",
    "\n",
    "    #print(img.shape) \n",
    "\n",
    "    scan_depth = int(img.shape[-1])\n",
    "    \n",
    "    # If depth is not 64, make it 64\n",
    "    if scan_depth < 64:\n",
    "        number = 64 - scan_depth\n",
    "        #print(number)\n",
    "        # take last image and stack\n",
    "        extend = [img[:,:,-1] for _ in range(number)]\n",
    "        extend = np.array(np.dstack(extend))\n",
    "        #print(extend.shape)\n",
    "        img = np.concatenate((img, extend), axis=2)\n",
    "    \n",
    "    else:\n",
    "        factor = int(np.floor(scan_depth / target_depth)) # min depth is 32\n",
    "        #print(factor) # floor and ceil result in different values\n",
    "        #print(scan_depth, factor)\n",
    "\n",
    "        flatten = []\n",
    "        idx = 0\n",
    "\n",
    "        for i in range(0, scan_depth, factor):\n",
    "          #print(idx)\n",
    "\n",
    "            if idx>=scan_depth:\n",
    "                break\n",
    "            else:\n",
    "                flatten.append(img[:,:,i])\n",
    "\n",
    "        img = np.array(np.dstack(flatten))\n",
    "\n",
    "    # hardcode from bottom, if not 64 slices\n",
    "    img = img[:,:,:target_depth]     \n",
    "\n",
    "    #print(\"Final shape: \", img.shape)\n",
    "    \n",
    "    # hardcode from bottom, if not 32 slices\n",
    "    img = img[:,:,:target_depth]\n",
    "\n",
    "    assert img.shape[-1] == target_depth , \"Error\"\n",
    "    return img\n",
    "\n",
    "\n",
    "# Spline interpolated zoom (SIZ)\n",
    "def change_depth_siz(img):\n",
    "    desired_depth = 64\n",
    "    current_depth = img.shape[-1]\n",
    "    depth = current_depth / desired_depth\n",
    "    depth_factor = 1 / depth\n",
    "    img_new = zoom(img, (1, 1, depth_factor), mode='nearest')\n",
    "    return img_new\n",
    "\n",
    "\n",
    "def normalize(image):\n",
    "    \n",
    "    MIN_BOUND = -1000 # anything below this we are not interested in \n",
    "    MAX_BOUND = 700   # anything above too\n",
    "\n",
    "    image = (image - MIN_BOUND) / (MAX_BOUND - MIN_BOUND)\n",
    "    image[image>1] = 1.\n",
    "    image[image<0] = 0.\n",
    "    return image\n",
    "\n",
    "\n",
    "def zero_center(image):\n",
    "    image = image - PIXEL_MEAN\n",
    "    return image\n",
    "\n",
    "\n",
    "def plot_seq(data, name):\n",
    "    \n",
    "    a, b = 3, 20\n",
    "    data = np.reshape(data, (a, b, 128, 128))\n",
    "    test_data = data\n",
    "    r, c = test_data.shape[0], test_data.shape[1]\n",
    "\n",
    "    cmaps = [['viridis', 'binary'], ['plasma', 'coolwarm'], ['Greens', 'copper']]\n",
    "\n",
    "    heights = [a[0].shape[0] for a in test_data]\n",
    "    widths = [a.shape[1] for a in test_data[0]]\n",
    "\n",
    "    fig_width = 10.  # inches\n",
    "    fig_height = fig_width * sum(heights) / sum(widths)\n",
    "\n",
    "    f, axarr = plt.subplots(r,c, figsize=(fig_width, fig_height),\n",
    "          gridspec_kw={'height_ratios':heights})\n",
    "\n",
    "    for i in range(r):\n",
    "        for j in range(c):\n",
    "            axarr[i, j].imshow(test_data[i][j], cmap='gray')\n",
    "            axarr[i, j].axis('off')\n",
    "    \n",
    "    plt.subplots_adjust(wspace=0, hspace=0, left=0, right=1, bottom=0, top=1)\n",
    "    plt.savefig('{}/{}.png'.format(log_path, name), dpi=1000)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# NOT USING THIS NOW\n",
    "def resize_depth_wise(img3d):\n",
    "    '''\n",
    "      Inputs a 3d tensor with uneven depth\n",
    "      Outputs a 3d tensor with even depth, in this case depth=64\n",
    "    '''\n",
    "  \n",
    "  # patient image 3D\n",
    "    p = img3d\n",
    "  # list of 3D slices of p\n",
    "    p_2d = []\n",
    "\n",
    "    depth = 2\n",
    "\n",
    "    n = 0\n",
    "    c = 0\n",
    "\n",
    "    for c in range(70):\n",
    "        img = img3d[:,:,n+depth]\n",
    "        p_2d.append(img)\n",
    "        n = n+depth\n",
    "        c = c+1\n",
    "        \n",
    "    p_3d_d64 = np.array(np.dstack(p_2d))\n",
    "    return p_3d_d64\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/hasib/uniformizing-3D/dataset/TrainingSet_1_of_2/'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Classes for data loading and preprocessing\n",
    "class ImageCLEFDataset:\n",
    "    def __init__(\n",
    "            self, \n",
    "            datadir, \n",
    "            csv_path,\n",
    "    ):\n",
    "        \n",
    "        # Patient names\n",
    "        self.ids = sorted(os.listdir(datadir))\n",
    "        # Sorted patient names\n",
    "        self.images_fps = [os.path.join(datadir, image_id) for image_id in self.ids]\n",
    "        \n",
    "        \n",
    "        # Read csv\n",
    "        csv = pd.read_csv(os.path.join(dataset_path, csv_path))\n",
    "        \n",
    "        # Get labels\n",
    "        svr_scores = csv['SVR_Severity'].values\n",
    "        svr_score_binary = []\n",
    "        for s in svr_scores:\n",
    "            if s == \"LOW\":\n",
    "                svr_score_binary.append(0)\n",
    "            if s == \"HIGH\":\n",
    "                svr_score_binary.append(1)\n",
    "        \n",
    "        svr_score_binary = np.array(svr_score_binary)\n",
    "        \n",
    "        num_classes = 2\n",
    "        # Onehot labels\n",
    "        self.labels = np_utils.to_categorical(svr_score_binary, num_classes)\n",
    "        \n",
    "        \n",
    "        \n",
    "    def __getitem__(self, i):\n",
    "        # Read data\n",
    "        img = sitk.ReadImage(self.images_fps[i])\n",
    "\n",
    "        # Get array\n",
    "        img = sitk.GetArrayFromImage(img)\n",
    "\n",
    "        # Resize x,y,z\n",
    "        img = rs_img(img)\n",
    "\n",
    "        # What downsampling technique?\n",
    "        img = change_depth_sss(img)\n",
    "        \n",
    "        gt = self.labels[i]\n",
    "        \n",
    "        return img, gt\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Dataloder(keras.utils.Sequence):\n",
    "    \"\"\"Load data from dataset and form batches\n",
    "    \n",
    "    Args:\n",
    "        dataset: instance of Dataset class for image loading and preprocessing.\n",
    "        batch_size: Integet number of images in batch.\n",
    "        shuffle: Boolean, if `True` shuffle image indexes each epoch.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, dataset, batch_size=1, shuffle=False):\n",
    "        self.dataset = dataset\n",
    "        self.batch_size = batch_size\n",
    "        self.shuffle = shuffle\n",
    "        self.indexes = np.arange(len(dataset))\n",
    "\n",
    "        self.on_epoch_end()\n",
    "\n",
    "    def __getitem__(self, i):\n",
    "        \n",
    "        # collect batch data\n",
    "        start = i * self.batch_size\n",
    "        stop = (i + 1) * self.batch_size\n",
    "        data = []\n",
    "        for j in range(start, stop):\n",
    "            data.append(self.dataset[j])\n",
    "        \n",
    "        # Transpose list of lists\n",
    "        batch = [np.stack(samples, axis=0) for samples in zip(*data)]\n",
    "        \n",
    "        return batch\n",
    "    \n",
    "    def __len__(self):\n",
    "        \"\"\"Denotes the number of batches per epoch\"\"\"\n",
    "        return len(self.indexes) // self.batch_size\n",
    "    \n",
    "    def on_epoch_end(self):\n",
    "        \"\"\"Callback function to shuffle indexes each epoch\"\"\"\n",
    "        if self.shuffle:\n",
    "            self.indexes = np.random.permutation(self.indexes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# image path, csv path\n",
    "dataset = ImageCLEFDataset(training_data_raw, '8ba42c65-534a-4cdf-b983-e7fcaaaa3002_TrainingSet_metaData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((128, 128, 64), (2,))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image, gt = dataset[55] \n",
    "image.shape, gt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f6a1b037be0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image[:,:,55])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataloader = Dataloder(dataset, batch_size=2, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 128, 128, 64) (2, 2)\n"
     ]
    }
   ],
   "source": [
    "# Test train loader\n",
    "x , y = next(iter(train_dataloader))\n",
    "print(x.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://www.kaggle.com/sentdex/first-pass-through-data-w-3d-convnet#Section-3:-Preprocessing-our-Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "for batch_idx, (features, targets) in enumerate(train_dataloader):\n",
    "    print(batch_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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