Tumor Segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Brain Tumor Segmentation\n",
    "\n",
    "This Notebook will train a convolution neural network to segment tumors in brain scans."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 1 Imports, Variables & Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import skimage.io as io\n",
    "import skimage.transform as trans\n",
    "import random as r\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "def create_data(src, mask, label=False, resize=(128,128,128)):\n",
    "    files = glob.glob(src + mask, recursive=True)\n",
    "    imgs = []\n",
    "    for file in files:\n",
    "        img = io.imread(file, plugin='simpleitk')\n",
    "        if label:\n",
    "            img[img == 4] = 1\n",
    "            img[img != 1] = 0\n",
    "            img = img.astype('float32')\n",
    "        else:\n",
    "            img = (img-img.mean()) / img.std()\n",
    "        img = trans.resize(img, resize, mode='constant')\n",
    "        imgs.append(img)\n",
    "    name = 'y' if label else 'x'\n",
    "    np.save(name, np.array(imgs)[..., np.newaxis].astype('float32'))\n",
    "    print('Saved', len(files), 'to', name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Input, Model\n",
    "from keras.layers import Conv2D, Concatenate, MaxPooling2D, Reshape\n",
    "from keras.layers import UpSampling2D, Activation, Permute\n",
    "\n",
    "def level_block(m, dim, depth, factor, acti):\n",
    "    if depth > 0:\n",
    "        n = Conv2D(dim, 3, activation=acti, padding='same')(m)\n",
    "        n = Conv2D(dim, 3, activation=acti, padding='same')(n)\n",
    "        m = MaxPooling2D()(n)\n",
    "        m = level_block(m, int(factor*dim), depth-1, factor, acti)\n",
    "        m = UpSampling2D()(m)\n",
    "        m = Conv2D(dim, 2, activation=acti, padding='same')(m)\n",
    "        m = Concatenate(axis=3)([n, m])\n",
    "    m = Conv2D(dim, 3, activation=acti, padding='same')(m)\n",
    "    return Conv2D(dim, 3, activation=acti, padding='same')(m)\n",
    "\n",
    "def UNet(img_shape, n_out=1, dim=64, depth=4, factor=2, acti='elu', flatten=False):\n",
    "    i = Input(shape=img_shape)\n",
    "    o = level_block(i, dim, depth, factor, acti)\n",
    "    o = Conv2D(n_out, (1, 1))(o)\n",
    "    if flatten:\n",
    "        o = Reshape(n_out, img_shape[0] * img_shape[1])(o)\n",
    "        o = Permute((2, 1))(o)\n",
    "    o = Activation('sigmoid')(o)\n",
    "    return Model(inputs=i, outputs=o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Input, Model\n",
    "from keras.layers import Conv3D, Concatenate, MaxPooling3D, Reshape\n",
    "from keras.layers import UpSampling3D, Activation, Permute\n",
    "\n",
    "def level_block_3d(m, dim, depth, factor, acti):\n",
    "    if depth > 0:\n",
    "        n = Conv3D(dim, 3, activation=acti, padding='same')(m)\n",
    "        m = MaxPooling3D()(n)\n",
    "        m = level_block_3d(m, int(factor*dim), depth-1, factor, acti)\n",
    "        m = UpSampling3D()(m)\n",
    "        m = Concatenate(axis=4)([n, m])\n",
    "    return Conv3D(dim, 3, activation=acti, padding='same')(m)\n",
    "\n",
    "def UNet_3D(img_shape, n_out=1, dim=64, depth=4, factor=2, acti='elu'):\n",
    "    i = Input(shape=img_shape)\n",
    "    o = level_block_3d(i, dim, depth, factor, acti)\n",
    "    o = Conv3D(n_out, 1, activation='sigmoid')(o)\n",
    "    return Model(inputs=i, outputs=o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import keras.backend as K\n",
    "def f1_score(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 + 1.) / (K.sum(y_true_f) + K.sum(y_pred_f) + 1.)\n",
    "\n",
    "def f1_loss(y_true, y_pred):\n",
    "    return -f1_score(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 2 Process Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "This will use the **T1c** files as input (features) and the **OT** files as output (labels). Note that the entire LGG folder as well as seperate color channels are currently excluded from this built due to performance bottlenecks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved 220 to x\n",
      "Saved 220 to y\n"
     ]
    }
   ],
   "source": [
    "create_data('BRATS2015_Training/HGG/', '**/*T1c*.mha', label=False, resize=(32,32,32))\n",
    "create_data('BRATS2015_Training/HGG/', '**/*OT*.mha', label=True, resize=(32,32,32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 3 Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x:  (220, 32, 32, 32, 1)\n",
      "y: (220, 32, 32, 32, 1)\n"
     ]
    }
   ],
   "source": [
    "x = np.load('x.npy')\n",
    "print('x: ', x.shape)\n",
    "y = np.load('y.npy')\n",
    "print('y:', y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's take a look at a few samples to check if the labels are correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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AmAIAGCBMAQAMsGdqh1z0K9d367f86bO69ZXb+3uYVs/p70GqH/T/U6/c08/Vqyf19zC1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115541eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random as r\n",
    "i = int(r.random() * x.shape[0])\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(121)\n",
    "plt.imshow(x[i, int(x.shape[1]/2), :, :, 0])\n",
    "plt.subplot(122)\n",
    "plt.imshow(y[i, int(y.shape[1]/2), :, :, 0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 4 Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "model = UNet_3D(x.shape[1:], dim=16, factor=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.load_weights('weights.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer=Adam(lr=0.000001), loss=f1_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 176 samples, validate on 44 samples\n",
      "Epoch 1/1\n",
      "176/176 [==============================] - 379s - loss: -0.5516 - val_loss: -0.5710\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1219526d8>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x, y, validation_split=0.2, epochs=1, batch_size=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "model.save_weights('weights.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "pred = model.predict(x[:50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Zi5wB/tZK65PaBKwn107Aai2pUWut7W+tnZvkrCRPTvJNSz1Aa+2i1trO1trO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\nlFjLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12327edd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AACAzNGoAAAAAkBkaNQAAAADIDI0aAAAAAGSGRg0AAAAAMkOjBgAAAACZoVED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\nmfn/nFQp1tJ++HEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123485748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GAAAwmBUbtaraXVUfrapPVNXVVfUz0/sfX1UfqarPV9VvV9XOzU8X4MvUJ2BE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\nmP8fFIYTlPgfqMwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1237fd9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random as r\n",
    "num = int(x.shape[1]/2)\n",
    "for n in range(3):\n",
    "    i = int(r.random() * pred.shape[0])\n",
    "    plt.figure(figsize=(15,10))\n",
    "\n",
    "    plt.subplot(131)\n",
    "    plt.title('Input')\n",
    "    plt.imshow(x[i, num, :, :, 0])\n",
    "\n",
    "    plt.subplot(132)\n",
    "    plt.title('Ground Truth')\n",
    "    plt.imshow(y[i, num, :, :, 0])\n",
    "\n",
    "    plt.subplot(133)\n",
    "    plt.title('Prediction')\n",
    "    plt.imshow(pred[i, num, :, :, 0])\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}