Brain_segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "--f6nwYUsMtB"
   },
   "source": [
    "#     >>>>>>...Brain Segmentation with CNN in MRI images...<<<<<<<\n",
    "                                              Rahul Kumar\n",
    "                                         Mechatronics (1911MT11)\n",
    "                                  Department of Mechanical Engineering\n",
    "                               Indian Institute of Technology, Patna, Bihar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "4x4PQfGFhh49",
    "outputId": "5993841d-bce1-45b7-e35d-ba5907368116"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "# Mounting google drive for using the BraTS 2015 training dataset\n",
    "from google.colab import drive      \n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "ms53i2rghDPp",
    "outputId": "4ade0440-0372-4233-89d5-41a14533549d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: simpleitk in /usr/local/lib/python3.6/dist-packages (1.2.4)\n"
     ]
    }
   ],
   "source": [
    "pip install simpleitk # Installations Required\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "7KS9QSv8hDPj",
    "outputId": "8db5c862-a1e3-487b-cab9-1e9c3595cb8c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Imports\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import random as r\n",
    "from keras import backend as K\n",
    "from utils import create_data, augmentation\n",
    "from Unet_model import  unet_model, dice_coef, dice_coef_loss\n",
    "from Simple_model import simple_model, dice_coef, dice_coef_loss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5w6f6Jn_DHUp"
   },
   "outputs": [],
   "source": [
    "# Calling GPU\n",
    "import keras.backend.tensorflow_backend as tfback\n",
    "\n",
    "def _get_available_gpus():  \n",
    "\n",
    "    if tfback._LOCAL_DEVICES is None:  \n",
    "        devices = tf.config.list_logical_devices()  \n",
    "        tfback._LOCAL_DEVICES = [x.name for x in devices]  \n",
    "    return [x for x in tfback._LOCAL_DEVICES if 'device:gpu' in x.lower()]\n",
    "\n",
    "tfback._get_available_gpus = _get_available_gpus\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Hd_BD8QDhDPv"
   },
   "outputs": [],
   "source": [
    "# Initialization\n",
    "K.set_image_data_format(\"channels_first\")                          # Input shape in the future will be (batch_size, no_of_channels, img_size, img_size)\n",
    "#K.set_image_dim_ordering(\"th\")\n",
    "img_size = 120      #original img size is 240*240\n",
    "num_epoch = 5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oq6JcDLUhDP9"
   },
   "outputs": [],
   "source": [
    "# Creating the data for segmented sequences and loading it in y\n",
    "create_data('/content/drive/My Drive/Datasets/BRATS2015_Training/HGG/', '**/*OT*.mha', label=True, resize=(155,img_size,img_size))\n",
    "y = np.load('y_{}.npy'.format(img_size))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AVUnCJw-hDQC"
   },
   "outputs": [],
   "source": [
    "# Creating the data for T2-flair sequences and loading it in x1\n",
    "create_data('/content/drive/My Drive/Datasets/BRATS2015_Training/HGG/', '**/*Flair*.mha', label=False, resize=(155,img_size,img_size))\n",
    "x1 = np.load('x_{}.npy'.format(img_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105
    },
    "colab_type": "code",
    "id": "C01-8ttRIT2u",
    "outputId": "1aa2041f-6bc7-4c01-c74b-f34e886ff605"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing--- **/*T1[^c]*.mha\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/numpy_array_iterator.py:136: UserWarning: NumpyArrayIterator is set to use the data format convention \"channels_last\" (channels on axis 3), i.e. expected either 1, 3, or 4 channels on axis 3. However, it was passed an array with shape (1, 1, 32, 32) (32 channels).\n",
      "  str(self.x.shape[channels_axis]) + ' channels).')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved 220 to x_32\n"
     ]
    }
   ],
   "source": [
    "# Creating the data for T1 weighted sequences and loading it in x2\n",
    "create_data('/content/drive/My Drive/Datasets/BRATS2015_Training/HGG/', '**/*T1[^c]*.mha', label=False, resize=(155,img_size,img_size))\n",
    "x2 = np.load('x_{}.npy'.format(img_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105
    },
    "colab_type": "code",
    "id": "jH6XTBcNIUIK",
    "outputId": "00e8955f-7f62-436d-8162-a89060430728"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing--- **/*T1c*.mha\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/numpy_array_iterator.py:136: UserWarning: NumpyArrayIterator is set to use the data format convention \"channels_last\" (channels on axis 3), i.e. expected either 1, 3, or 4 channels on axis 3. However, it was passed an array with shape (1, 1, 32, 32) (32 channels).\n",
      "  str(self.x.shape[channels_axis]) + ' channels).')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved 220 to x_32\n"
     ]
    }
   ],
   "source": [
    "# Creating the data for T1c weighted sequences and loading it in x3\n",
    "create_data('/content/drive/My Drive/Datasets/BRATS2015_Training/HGG/', '**/*T1c*.mha', label=False, resize=(155,img_size,img_size))\n",
    "x3 = np.load('x_{}.npy'.format(img_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105
    },
    "colab_type": "code",
    "id": "tZC20lA4IUYX",
    "outputId": "3d551bad-8027-43dd-9992-932583ee7cd6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing--- **/*T2*.mha\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/numpy_array_iterator.py:136: UserWarning: NumpyArrayIterator is set to use the data format convention \"channels_last\" (channels on axis 3), i.e. expected either 1, 3, or 4 channels on axis 3. However, it was passed an array with shape (1, 1, 32, 32) (32 channels).\n",
      "  str(self.x.shape[channels_axis]) + ' channels).')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved 220 to x_32\n"
     ]
    }
   ],
   "source": [
    "# Creating the data for T2 weighted sequences and loading it in x4\n",
    "create_data('/content/drive/My Drive/Datasets/BRATS2015_Training/HGG/', '**/*T2*.mha', label=False, resize=(155,img_size,img_size))\n",
    "x4 = np.load('x_{}.npy'.format(img_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "wN-LY4AFhDQI",
    "outputId": "540bb83a-9640-486e-df1c-4c5681cc0601"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of the loaded data is :  (35200, 1, 120, 120) (35200, 1, 120, 120)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summary of the data loaded\n",
    "plt.imshow(x1[0,0,:,:])\n",
    "print(\"shape of the loaded data is : \",y.shape, x1.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                                  __These are the four pulse sequences of each patient__\n",
    "![These are the four pulse sequences of each patient](Assets/all.JPG)</br>\n",
    "            _ _ _ _ _ _ _ _ _ _ _ _ _ _ _   This are the 155 slices per MRI pulse sequences._ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n",
    "![](Assets/slices.gif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "zo8q5Li28VVR",
    "outputId": "cc145d1f-8f5c-4a69-c1c0-f1681c596248"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of the input = (batch_size, no_of_channels, img_size, img_size) (35200, 4, 32, 32)\n"
     ]
    }
   ],
   "source": [
    "# Now creating a variable x for storing all four modalities/sequences/channels in one \n",
    "x = np.zeros((35200, 4, img_size,img_size))\n",
    "x[:,0,:,:] = x1[:,0,:,:]\n",
    "x[:,1,:,:] = x2[:,0,:,:]\n",
    "x[:,2,:,:] = x3[:,0,:,:]\n",
    "x[:,3,:,:] = x4[:,0,:,:]\n",
    "\n",
    "print(\"shape of the input = (batch_size, no_of_channels, img_size, img_size)\", x.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "colab_type": "code",
    "id": "Yru0tdT5hDQb",
    "outputId": "c7fb5c49-e77c-4ea8-805c-a0656adb91e6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "35200/35200 [==============================] - 279s 8ms/step - loss: -0.7025 - dice_coef: 0.7025\n",
      "Epoch 2/5\n",
      "35200/35200 [==============================] - 276s 8ms/step - loss: -0.8633 - dice_coef: 0.8633\n",
      "Epoch 3/5\n",
      "35200/35200 [==============================] - 276s 8ms/step - loss: -0.8863 - dice_coef: 0.8863\n",
      "Epoch 4/5\n",
      "35200/35200 [==============================] - 275s 8ms/step - loss: -0.8975 - dice_coef: 0.8975\n",
      "Epoch 5/5\n",
      "35200/35200 [==============================] - 276s 8ms/step - loss: -0.9156 - dice_coef: 0.9156\n"
     ]
    }
   ],
   "source": [
    "#training on Unet model\n",
    "model = unet_model()\n",
    "history = model.fit(x1, y, batch_size=32 ,nb_epoch= num_epoch, verbose=1, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "K97tGWKghDQg"
   },
   "source": [
    "#### "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "_2yKoLb3hDQh",
    "outputId": "9e461164-f567-4ef0-fc50-8665275c89e3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=float32, numpy=0.8450317>"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction on Unet model\n",
    "y_pred = model.predict(x1[10000:20000,:,:,:])\n",
    "y_true = y[10000:20000,:,:,:]\n",
    "dice_coef(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "DwyyEkhg9j93",
    "outputId": "05e5be35-6d2b-4ac7-dbdf-782e445bded6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.1801 - dice_coef: 0.1801\n",
      "Epoch 2/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.2067 - dice_coef: 0.2067\n",
      "Epoch 3/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.2125 - dice_coef: 0.2125\n",
      "Epoch 4/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.2175 - dice_coef: 0.2175\n",
      "Epoch 5/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.2209 - dice_coef: 0.2209\n",
      "Epoch 6/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.2257 - dice_coef: 0.2257\n",
      "Epoch 7/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.2327 - dice_coef: 0.2327\n",
      "Epoch 8/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.2422 - dice_coef: 0.2422\n",
      "Epoch 9/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.2545 - dice_coef: 0.2545\n",
      "Epoch 10/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.2711 - dice_coef: 0.2711\n",
      "Epoch 11/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.2901 - dice_coef: 0.2901\n",
      "Epoch 12/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.3140 - dice_coef: 0.3140\n",
      "Epoch 13/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.3400 - dice_coef: 0.3400\n",
      "Epoch 14/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.3713 - dice_coef: 0.3713\n",
      "Epoch 15/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.3972 - dice_coef: 0.3972\n",
      "Epoch 16/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.4242 - dice_coef: 0.4242\n",
      "Epoch 17/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.4530 - dice_coef: 0.4530\n",
      "Epoch 18/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.4833 - dice_coef: 0.4833\n",
      "Epoch 19/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.5098 - dice_coef: 0.5098\n",
      "Epoch 20/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.5614 - dice_coef: 0.5614\n",
      "Epoch 21/35\n",
      "35200/35200 [==============================] - 51s 1ms/step - loss: -0.6289 - dice_coef: 0.6289\n",
      "Epoch 22/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.7142 - dice_coef: 0.7142\n",
      "Epoch 23/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.7793 - dice_coef: 0.7793\n",
      "Epoch 24/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.8205 - dice_coef: 0.8205\n",
      "Epoch 25/35\n",
      "35200/35200 [==============================] - 51s 1ms/step - loss: -0.8631 - dice_coef: 0.8631\n",
      "Epoch 26/35\n",
      "35200/35200 [==============================] - 51s 1ms/step - loss: -0.8935 - dice_coef: 0.8935\n",
      "Epoch 27/35\n",
      "35200/35200 [==============================] - 51s 1ms/step - loss: -0.9103 - dice_coef: 0.9103\n",
      "Epoch 28/35\n",
      "35200/35200 [==============================] - 51s 1ms/step - loss: -0.9238 - dice_coef: 0.9238\n",
      "Epoch 29/35\n",
      "35200/35200 [==============================] - 51s 1ms/step - loss: -0.9282 - dice_coef: 0.9282\n",
      "Epoch 30/35\n",
      "35200/35200 [==============================] - 51s 1ms/step - loss: -0.9399 - dice_coef: 0.9399\n",
      "Epoch 31/35\n",
      "35200/35200 [==============================] - 51s 1ms/step - loss: -0.9436 - dice_coef: 0.9436\n",
      "Epoch 32/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.9492 - dice_coef: 0.9492\n",
      "Epoch 33/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.9501 - dice_coef: 0.9501\n",
      "Epoch 34/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.9540 - dice_coef: 0.9540\n",
      "Epoch 35/35\n",
      "35200/35200 [==============================] - 52s 1ms/step - loss: -0.9530 - dice_coef: 0.9530\n"
     ]
    }
   ],
   "source": [
    "# Training on Simple Model\n",
    "model = simple_model()\n",
    "history = model.fit(x1, y, batch_size=32, epochs=35, verbose = 1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "p7Rr92xeaDKO",
    "outputId": "9c0311fa-c437-4e54-f565-349915f1993c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'dice_coef'])\n"
     ]
    }
   ],
   "source": [
    "# list all data in history\n",
    "print(history.history.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oBt14BB-awsa"
   },
   "outputs": [],
   "source": [
    "# save model and weights\n",
    "model.save('aug{}_{}_epoch{}'.format(num_of_aug,img_size,num_epoch))\n",
    "model.save_weights('weights_{}_{}.h5'.format(img_size,num_epoch))\n",
    "#model.load_weights('weights.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "jkA7u-msa1JL",
    "outputId": "0114da9b-6a47-4014-e06d-e752e27ab032"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize history for accuracy\n",
    "plt.plot(history.history['dice_coef'])\n",
    "plt.title('model dice_coef per epoch for HGG core')\n",
    "plt.ylabel('dice_coef')\n",
    "plt.xlabel('epoch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "Zgh5Z4KH_dtk",
    "outputId": "689e5921-5156-4cb8-bedb-74221e4b58a5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=float32, numpy=0.84367526>"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction on simple model\n",
    "y_pred = model.predict(x1[0:10000,:,:,:])\n",
    "y_true = y[0:10000,:,:,:]\n",
    "dice_coef(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "colab_type": "code",
    "id": "2NfioA03gzVU",
    "outputId": "90eda450-810d-448e-a415-5f5eedd9e05a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f3b308f5c18>"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Printing the original MRI slice , segmented slice and predicted segmented slice\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.subplot(1,3,1)\n",
    "plt.title('Brain MRI sequence')\n",
    "plt.imshow(x1[9530,:].reshape((120,120)))\n",
    "plt.subplot(1,3,2)\n",
    "plt.title('original segmented tumour')\n",
    "plt.imshow(y[9530,0,:,:])\n",
    "plt.subplot(1,3,3)\n",
    "plt.title('predicted segmented tumor')\n",
    "plt.imshow(y_pred[9530,:].reshape((img_size,img_size)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UTXPS68_J-c1"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "machine_shape": "hm",
   "name": "Brain_segmentation.ipynb",
   "provenance": []
  },
  "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}