802 lines (801 with data), 282.2 kB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import skimage.io as io\n",
    "import skimage.color as color\n",
    "import random as r\n",
    "import math\n",
    "from keras.models import Model\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
    "from keras.layers import concatenate, Conv2D, MaxPooling2D, Conv2DTranspose\n",
    "from keras.layers import Input, merge, UpSampling2D,BatchNormalization\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.optimizers import Adam\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n0: other\\n1: necrosis + NET\\n2: edema\\n4: enhancing tumor\\n5: full tumor\\n'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K.set_image_dim_ordering(\"th\")\n",
    "\n",
    "\n",
    "img_size = 240      #original img size is 240*240\n",
    "smooth = 0.005 \n",
    "num_of_aug = 2\n",
    "num_epoch = 30\n",
    "pul_seq = 'Flair'\n",
    "sharp = False       # sharpen filter\n",
    "LR = 1e-4\n",
    "\n",
    "num_of_patch = 4 #must be a square number\n",
    "label_num = 5   # 1 = necrosis+NET, 2 = tumor core,3= original, 4 = ET, 5 = complete tumor\n",
    "'''\n",
    "0: other\n",
    "1: necrosis + NET\n",
    "2: edema\n",
    "4: enhancing tumor\n",
    "5: full tumor\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# function to read all data (training and label) and transform into numpy array\n",
    "import glob\n",
    "def create_data(src, mask, label=False, resize=(155,img_size,img_size)):\n",
    "    files = glob.glob(src + mask, recursive=True)\n",
    "    r.seed(9)\n",
    "    r.shuffle(files)    # shuffle patients\n",
    "    imgs = []\n",
    "    print('Processing---', mask)\n",
    "    for file in files:\n",
    "        img = io.imread(file, plugin='simpleitk')\n",
    "        #img = trans.resize(img, resize, mode='constant')\n",
    "        if label:\n",
    "            if label_num == 5:\n",
    "                img[img != 0] = 1       #Region 1 => 1+2+3+4 complete tumor\n",
    "            if label_num == 1:\n",
    "                img[img != 1] = 0       #only left necrosis and NET\n",
    "            if label_num == 2:\n",
    "                img[img == 2] = 0       #turn edema to 0\n",
    "                img[img != 0] = 1       #only keep necrosis, ET, NET = Tumor core\n",
    "            if label_num == 4:\n",
    "                img[img != 4] = 0       #only left ET\n",
    "                img[img == 4] = 1\n",
    "            if label_num == 3:\n",
    "                img[img == 3] = 1       # remain GT, design for 2015 data\n",
    "                \n",
    "                \n",
    "            img = img.astype('float32')\n",
    "        else:\n",
    "            img = (img-img.mean()) / img.std()      #normalization => zero mean   !!!care for the std=0 problem\n",
    "            img = img.astype('float32')\n",
    "        for slice in range(60,130):     #choose the slice range\n",
    "            img_t = img[slice,:,:]\n",
    "            img_t =img_t.reshape((1,)+img_t.shape)\n",
    "            img_t =img_t.reshape((1,)+img_t.shape)   #become rank 4\n",
    "            #img_g = augmentation(img_t,num_of_aug)\n",
    "            for n in range(img_t.shape[0]):\n",
    "                imgs.append(img_t[n,:,:,:])\n",
    "    \n",
    "    return np.array(imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#function to read one subject data\n",
    "def create_data_onesubject_val(src, mask,count, label=False):\n",
    "    files = glob.glob(src + mask, recursive=True)\n",
    "    r.seed(9)\n",
    "    r.shuffle(files)    # shuffle patients\n",
    "    k = len(files) - count -1\n",
    "    imgs = []\n",
    "    file = files[k]\n",
    "    print('Processing---', mask,'--',file)\n",
    "    \n",
    "    img = io.imread(file, plugin='simpleitk')\n",
    "    #img = trans.resize(img, resize, mode='constant')\n",
    "    if label:\n",
    "        if label_num == 5:\n",
    "            img[img != 0] = 1       #Region 1 => 1+2+3+4 complete tumor\n",
    "        if label_num == 1:\n",
    "            img[img != 1] = 0       #only left necrosis\n",
    "        if label_num == 2:\n",
    "            img[img == 2] = 0       #turn edema to 0\n",
    "            img[img != 0] = 1       #only keep necrosis, ET, NET = Tumor core\n",
    "        if label_num == 4:\n",
    "            img[img != 4] = 0       #only left ET\n",
    "            img[img == 4] = 1\n",
    "        img = img.astype('float32')\n",
    "    else:\n",
    "        img = (img-img.mean()) / img.std()      #normalization => zero mean   !!!care for the std=0 problem\n",
    "        img = img.astype('float32')\n",
    "    for slice in range(155):     #choose the slice range\n",
    "        img_t = img[slice,:,:]\n",
    "        img_t =img_t.reshape((1,)+img_t.shape)\n",
    "        img_t =img_t.reshape((1,)+img_t.shape)   #become rank 4\n",
    "        #img_g = augmentation(img_t,num_of_aug)\n",
    "        for n in range(img_t.shape[0]):\n",
    "            imgs.append(img_t[n,:,:,:])\n",
    "    \n",
    "    return np.array(imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing--- **/*flair.nii.gz -- C:/brain_tumor/BRATS2018/HGG\\Brats18_CBICA_ASG_1\\Brats18_CBICA_ASG_1_flair.nii.gz\n",
      "Processing--- **/*t1ce.nii.gz -- C:/brain_tumor/BRATS2018/HGG\\Brats18_CBICA_ASG_1\\Brats18_CBICA_ASG_1_t1ce.nii.gz\n",
      "Processing--- **/*t1.nii.gz -- C:/brain_tumor/BRATS2018/HGG\\Brats18_CBICA_ASG_1\\Brats18_CBICA_ASG_1_t1.nii.gz\n",
      "Processing--- **/*t2.nii.gz -- C:/brain_tumor/BRATS2018/HGG\\Brats18_CBICA_ASG_1\\Brats18_CBICA_ASG_1_t2.nii.gz\n",
      "Processing--- **/*seg.nii.gz -- C:/brain_tumor/BRATS2018/HGG\\Brats18_CBICA_ASG_1\\Brats18_CBICA_ASG_1_seg.nii.gz\n",
      "Processing--- **/*seg.nii.gz -- C:/brain_tumor/BRATS2018/HGG\\Brats18_CBICA_ASG_1\\Brats18_CBICA_ASG_1_seg.nii.gz\n",
      "Processing--- **/*seg.nii.gz -- C:/brain_tumor/BRATS2018/HGG\\Brats18_CBICA_ASG_1\\Brats18_CBICA_ASG_1_seg.nii.gz\n",
      "Processing--- **/*seg.nii.gz -- C:/brain_tumor/BRATS2018/HGG\\Brats18_CBICA_ASG_1\\Brats18_CBICA_ASG_1_seg.nii.gz\n"
     ]
    }
   ],
   "source": [
    "#read one subject to show slices\n",
    "count = 106\n",
    "pul_seq = 'flair'\n",
    "Flair = create_data_onesubject_val('C:/brain_tumor/BRATS2018/HGG/', '**/*{}.nii.gz'.format(pul_seq), count, label=False)\n",
    "pul_seq = 't1ce'\n",
    "T1c = create_data_onesubject_val('C:/brain_tumor/BRATS2018/HGG/', '**/*{}.nii.gz'.format(pul_seq), count, label=False)\n",
    "pul_seq = 't1'\n",
    "T1 = create_data_onesubject_val('C:/brain_tumor/BRATS2018/HGG/', '**/*{}.nii.gz'.format(pul_seq), count, label=False)\n",
    "pul_seq = 't2'\n",
    "T2 = create_data_onesubject_val('C:/brain_tumor/BRATS2018/HGG/', '**/*{}.nii.gz'.format(pul_seq), count, label=False)\n",
    "label_num = 5\n",
    "Label_full = create_data_onesubject_val('C:/brain_tumor/BRATS2018/HGG/', '**/*seg.nii.gz', count, label=True)\n",
    "label_num = 2\n",
    "Label_core = create_data_onesubject_val('C:/brain_tumor/BRATS2018/HGG/', '**/*seg.nii.gz', count, label=True)\n",
    "label_num = 4\n",
    "Label_ET = create_data_onesubject_val('C:/brain_tumor/BRATS2018/HGG/', '**/*seg.nii.gz', count, label=True)\n",
    "label_num = 3\n",
    "Label_all = create_data_onesubject_val('C:/brain_tumor/BRATS2018/HGG/', '**/*seg.nii.gz', count, label=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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43W70+310Oh3kcjk0m030ej2Uy2Ukk0m43W50Oh10Oh30ej00Gg10u12p3ANAt9tFt9uV\nCyQfr9lsYnZ2FidPnsTVq1cRjUYxHo8laWOw5L/zsZxOJ3w+H5xOp/w/wzDg9/vxnve8B8eOHcO5\nc+d2VUMVZb9x9uxZmfEMBAKYmppCLpeDy+VCq9VCvV6Hz+cDcLs7wMq/ZVlot9twOp3w+/1wuVxS\nlOS8TiAQwGg0QrPZRCAQgGmaaLfbEtc8Hg88Ho/ERF5kaTxE6XMwGEQwGEQ+n4fT6YTX68VoNMIH\nP/hB9Ho9jWnKvmZxcVEMtQaDAQzDkO6a1+tFu92G1+uF0+mE2+3GaDQSKSW7b4ZhYDgcSgERuHUG\nh8MhnE4ner0efD4f8vk8VldX8cILL8g9kA2g0WgE0zRFOcKipsvl2iXVDAaDMtcajUZx/Phx1Ov1\nHzozrvxgNIHbY86ePYvl5WVMTEzANE14vV4xTBgMBpibm5MD2Gq10Ov14HQ6MRwO4fF44HK5pAPH\nxG00GmE4HKLX68kBcrlcknBls1kJgrVaTR6fl0/qmln55GGjbAwAkskkpqamcOnSJZGmPP7448jn\n83jllVf2+KeqKP+ew4cP48iRIzKgPRgMsLGxgdFohNnZWUm8SDAYxHg8FvkVZVfj8VhkXp1OR/4J\n3JJwjcdjGeaenJxEsVhEKBRCq9XCaDSCw+GA3+9HtVrFcDiUX3SPZeDjxdbv92M4HOL111/HzMwM\nzpw5g/e+9734q7/6K61gKvuOo0ePwjRNdLtdtFotDIdDxONxNBoNKYSwu+3z+STpYqGEc3Dj8Rjj\n8XhXl7rf76NarYrMnzOkVIOwgDkYDNBsNuWC6fP5pKCZSCTQ7/exs7ODVColcZSz37FYDF6vF16v\nF4uLi6jVaiiVSnv281SU78fS0hJM04RpmggGg2g2mxJLXC6XdNiCwSDa7TYGg4EkTy6XC06nU0Zv\nGNsYz/r9PlwuF+r1OiqVCtbW1hAIBPC1r31NTE44QuDz+eBwOMR4q91uIxKJSMxkE4DnHICcQypZ\nfD4farUatre39+rHeSDRBG6P+Omf/mkcO3YMx44dE4mU3++HZVm4cOECOp0O5ufnZQbA5XLJTFso\nFJK2t9/vRzwel7Z3NBpFrVZDOByW7hzd8xwOB8LhMG7evClyFnbXDMPA9va2OFZmMhnU63X4/X5k\nMhmp4DB5pHteNBoVd69Wq4Xnn38eMzMzaLVaOien7AvOnj2LlZUVtFotnD9/HoZhIJ/PIxaLIRAI\noNfr4dKlS3C73fB6vWg0GtjZ2ZHAxKSO8zQMkg6HA6PRSIoerPInk0kMBgNsbW0hm81ibW0Nfr8f\n/X4f8/PzmJycRKlUwsbGBhqNBra2tjAYDFAsFqVKalkW/H4/ksmkdAW63S42Nzexs7ODkydP4vd+\n7/dw8+ZNXLt2Df/1v/7XPf4pK/c7hw8fxtzcHILBIHK5HBwOB+r1OmZmZnDt2jU0m02YpolGo4F0\nOo1+v4/19XVkMhlYloWtrS34fD5MTEygVqvBMAw0Gg1RjPBc9Pt91Go1mQW3LAu9Xg/j8RiWZWFi\nYgKWZclMXSQSQa/Xw7Fjx/DCCy+gWq0iFAohFouhWCwCgChcFhYWUKlUZLY1HA4jEong9OnTKBQK\nanii7DlnzpxBu92W+5w9/rDw0W630e124fV64XA44PV6UavVpGjCc+P3+wFADLm8Xi9arRZ8Ph9a\nrZYUSM6dO4e5uTns7OxgZmZGmgSWZaHRaIgEMxqNiqFeKBQCcKsww+5eq9WSr0l5s8vlgsvlwtTU\nFBYXFwEA3/zmN/fmh3vA0ARujzh06BCOHj2KxcVFqQB6vV40m004HA70+32xT+50OtJpY8WS3QEG\nN+qLaTJiWZbM0dB4gQkddf7ValWqjazEALdklQxylHS5XC7EYjE4nU602214PB6prEYiEfj9fly7\ndg3T09PSStcETtkPPPzwwyiVSsjlcuJe53A44HK5RCpZLpcxHo/hcrng8XhQr9elwk88Ho9YmwPY\nVcHkLAFNg3j2ACCVSqHX60nSFg6HEQqF5Dy3220psIxGI5GZsEpKeRm7Cj6fD9VqFaPRCJcvX5Zz\nrSh7yczMDHw+H+r1usiuUqkUGo2GJGJerxelUgmLi4viKMnOAQuRVJq4XC643W6Ypilz2dFoFABE\nQul2u6WgaBgGYrEYlpaWMBgMJBHL5/NYXl5Gp9PB1NSUrOtYWFiA1+tFpVLBYDDAcDhEo9GA1+uV\nLrjL5UKtVhOHPUXZayzLQiAQgN/vF8MQh8Mh5nXValXUHCxCcp6t3+/LLOloNBJFCOfk3G63xB+v\n1yujAQ6HA5VKRWZRg8EgQqEQDMNArVZDJBIRcyDK/zmDSgMTzqJS7UXZp8/nQyQSkXsv46byH+PY\n6ydwv/Kud70LCwsLoiWORCJin0xLV17ieAij0ah06gKBAEKhkFi4sjVOCQqDYrfblR06vV4PABAO\nh+Wfi4uLiEQiCAQCUsXh445GI/T7fZG7cDccOxI+n09kZ3TApNzykUcewSc/+ck9+/kqCqE0g3Mv\nwK2ZUb7f7XsTaXDgcDhkjpT/DUAq/QBkLjQcDiORSEggpMTS6/ViOBwiEAhgdXUVr7zyCq5cuYLX\nX38dV69eFXkJzzLlyYZhyGA4vxaLOTQuKhaL8Pl86PV62NzcxGOPPbYHP1lFuY3f75c5UcYangn+\nN2drKMd/qxMeFR7snqVSqV3xajQaodFooFgsotvtytnx+/1wu90yz+3z+WCaphQ/6vU6arWaKFoC\ngYB0KUKhkMyGVyoVMSYqlUro9Xqo1+vY2dnB1NQUlpaW9uaHqyjfIxwOIxgMiiM574csvHNlBhMl\nrgDw+XwyTuPxeGQGFbgdD10u1y6zEwCIRCK7CjPtdlvcz/m4VKZUq1VpFgwGg11+DQB2NRxYIKE5\nXzKZFKn1Bz7wgbv8Uz2YaAfuLvLkk08iHo8jFotheXkZXq8X5XIZiUQCmUwG5XIZly9fxpUrV2Tf\nG5dxh8Nhqawkk0nkcjmRUG5vb6PdbmN1dRXFYlGGVHn54wHnQWQ7/dixY1KFpEyTWn/OznFVAR9r\nOBwiFovBsixEIhGUSiX5sPB4PHjooYfE1OHMmTP46le/ip2dHTz//PP41Kc+tWc/e+X+4mMf+xje\n/e534+rVq/jud7+Ler0uxQwakSQSCSSTSdy8eRNra2sAIDMFlJtQu8/Eye12IxqNIh6P45FHHhGt\nf7PZlCRwdXUVg8EAx48fx3g8RjgcxhtvvIE33ngDpVJJJJHU/geDQSnEUH7Cama5XJbOBQfUnU6n\nfD+0W282mzh16hTC4TAmJibwd3/3d3v8Cij3A4cOHUIymUQkEpHl9g6HA/F4HJubmxiNRpiYmMBo\nNMLc3BxGoxEymQwuXLggDnZ2IwQAcrl7//vfj/X1dbz66qswDAOmaWJlZUVm2AzDQDwex87ODgqF\nAnK5HGKxGDY2NtBqtRAOhxGNRrG+vi7FRY4MRCIRMVugnBK4tSbk2rVrePe7341qtYrz58/LvOrl\ny5fxyCOPiAxtZWUFX/jCF/bsZ6/cP/zUT/2UxCGuASiXy7K6pt/vY2trC61WC7FYTAx83jp7xiIg\nJY1Ucng8HnFS5jgOV3qsra0hGo3KPY9Sy36/j3A4jGQyiV6vB4/Hs0uxMhwOAdxO2oLBIPr9vjQj\n6vW6dOcA4M0330Qmk8GRI0dQqVTw2GOPwTAMVCoV3Yn6A9AO3F3ixIkTyGazmJ+fx8zMDAqFgrS4\ngdsOdAwq8XgcpmnKQGmpVJLWd61W26Uj5j4OSrcopWw0GtKFS6fTSCQSsiYgHA6j0Wggn8/LwDYA\nSdjC4TAmJyely8ZAy8NI2Rg7f1xsbJomMpnMLjv0mZkZzM7O4jd+4zf27Oev3D/Mzs7i7Nmz6Pf7\nshaAA93sULPa32q1UCgUMBgMpOpPWQeDGYOMw+FAMBhELBYTt9hqtYqrV6/i/PnzWF1dxcbGBt58\n803867/+K5577jlxuqOzJDt5nU5HZlMp7WRl1B5w+Xw6nY4MhbMr0O/3d8knOQvUbrfxkY985K7/\n3JX7i2AwiOnpaSnwccF9JBJBuVyGYRgyF0qrczqvUiblcDjkotfv98Xe/OjRo7hy5QrW19fx4IMP\n4tixY5icnESn00E+n5cKf6/XQyqVQjabFXdLAFII4Zqder2OQqEgZyQSicgcnsfjkTEAANjY2MDa\n2homJycxNTWFZrMpXfWpqSmcOHECrVYLOzs7eOaZZ/bgJ6/cTxw5ckQKdTQa4coMxgrK/VmkZwLG\noh8VVJZlibySuN1uxONxOT/soFOBkkqlMDExAZ/PJ2fV5XLJeaE80z7mQwMuzqVyZo7/zbskcFtd\nwl2rlF/SKdPv9+P973//3fyRHxg0gbsLnDp1CplMBul0GhMTE0ilUrh69SpWV1elml4ulwHcuoTF\nYjHZBccdOdT/8w3NQ8jDwb0bnU5HXLsWFxexuLgoiWM2m5WLaTQahd/vlwDb6XTQaDQQiUTg9Xrl\nsDabTamcUNZJzTWTOGqiE4mEzD3Yd/sMh0OcOHECR48exe/8zu/s5Uuh3OPMz8/jiSeewEsvvYSX\nX34ZvV5P9ijyvBiGAcMwUCwWRVrJRIj73+xJFIMR51QpMX7ppZfwxhtv4ObNm6jVami1WiIZGQwG\nuHjxIt5880243W4kEgmYpol4PI5wOIxYLIZ+v492u71rPQG/BuftOJvAC+twOBTjFD4vwoDJOYhf\n/MVfvOs/f+X+IBgM4vDhwwiFQjKPzUX0nOXmLHQwGJS4wk42FSa8SPJiyOSq0+mg3+8jk8nI1xwM\nBiiXy1K8oPMrL6mc96Zss91uo9/vy2wd5+Q4K7ewsIBwOCxOzPYCyqVLl1Cv17GysgKn04lisQin\n04lvfetbWFhYwMzMDMrlMkqlkiZxyh3jyJEjmJ2dRaPRgMfjEXdXJmKMCxy1oRs55fdsANCEjncy\nusGyAM95OLsDJc8ti/5UcrFgw+dh77QxJnHGDoCoRvj7AKTgz8YBV1YBkA6f/X7b6/Xwvve9bw9e\ngf2NLvK+wzz11FN4+OGHMT09La1vyg1XV1cRDodx5swZeUM///zzaDQamJqakoPjdrsl4HF4tdVq\nyVAo3SNZ8eTeHL752W5nFb9UKuGVV15BJpORJC6RSIjjFpOueDwuMk/u8mAHolariXUsEz8AePHF\nF2UlQjQalZk+ts7H4zEuXLiAT3ziE3v8ytx59tvSU+DePmvPPPMMZmZmcPHiRek8B4NBPPTQQ2LD\nTwdWwzCQzWaRyWRQqVREqsz3vj2oAbfnc3jBY/eOwYvLUXkGgVszQbFYDM8++yy2t7dx8eJFlEol\nmVHlGoFeryfyFS4+ZueNS8P5NeyJ5NzcHBwOBy5fvox6vS5SaLsDbTQaxWc+85k9eDXuLvvtrN3L\n5+zo0aM4fPiwfK5zno2yrEajIV2C8XiM+fl5cbVj54CdtkqlIis2EokEAEjcopkIZcg0+5menkar\n1cLk5KTEuvX1dVkKzs4gY+Di4iLW1tZgWRY2NjYk1j300EOo1Wq4ePGiFFS4WqRSqSASiWB6ehpO\npxPXr19HMplEsVjExMQEHnzwQXz5y18WJ9ulpSX89V//9R6/MncePWd3j/e9733i5JpOp2XFDM22\n7DNkNMNit5hF9maziVgshlQqJTGRcEat3++LRJ/5AJNDAJIAMrFrNBoYDAZSwGeiGAqFZNaV89/s\nsjmdTgSDQXGXHY/HktCx8cBRIRqhUPnCVVb1eh2GYeAb3/jGXX4l7j5v95xpB+4O8+CDDyKbzSKd\nTiOTycA0TQQCARSLRZk/A25VNDk7kEwmpRrCBMu+aJGXPh42avLp5MNKC6v+HHC1W7ZyYTGXBedy\nOTQaDRQKBeTzeRSLRZRKJfT7fZimuesy22w2xYWSQ7HFYhHValXmBdgR5HAqh8hbrRZWVlZ0Hk55\nx1laWsKbb76JSqWCcrks5+PmzZtot9tSkOD7mMUMmhkAEPlIv9/HaDQSUxMOdzPotVotALclYL1e\nTxJDVitTqRSSySSCwSDm5+cRj8flOQUCASSTSSSTSemsU/Jid7W0Syp58WVXrlqtotfrScLIAM1k\nlKtCnn5j6EyuAAAgAElEQVT66bv6Oij3NtPT03IO6vW6FBbC4bCMAgSDQeloDQYD1Go1ufjxAkdn\nSsMwRMYYDofFLCsYDCKTyYhKxLIsiY0ej0fW6tiLlaZpYnl5WYqlTBwzmYxcdinnqtfr8lgej0dc\n9SitTKfTWF1dxeLiIjKZjBRZWq0WSqUS/H6/FF/q9bp24pR3FHogMG5RbcH7GxfXsxjyVlUG1weE\nw2GJB3ZlCQuBgUBAzgaXgjN5syxLjEko2bTvauRoAJ0naXrHr89Ezy6hpKlJv98Xqad97pvdN5qH\nsQkRDofhcDhw5syZu/tC7GM0gbuDrKysIJvNIpVKAQDS6TRM00QqlZIqitvtxs7OjlTjQ6EQIpHI\nrp0eDDZsYXc6HUnkuKARgHTqgsGgJFicteH/ByAHjEOudtvWRqOBer0u1dVCoYB6vS5uk41GQxI0\nft1AIIB+vy/VH/5+MplELBZDMBiUzl0mk4HX68WRI0fwN3/zN3fnhVDueTKZDK5du4ZCoQAAEjBa\nrZYkdHYzHgYiWpYz+eG/EwZDBhh2yXg++XVCoZCsB2CXjG53pVIJr776Kl577TXk83mUy2VUq1Xp\nSrOr3uv1xMGLwZEyEwY1Os7y+bTbbfksYNeO87C1Wk0uzk899dTdf1GUew46xlHmxJkyrufgyhsm\nYNzBxpjByxsvnkzAKMOnM+vi4iIefPBBHD9+XDpuduc9rrSh0dBwOMTy8jIOHz4sksdSqYRGo4H1\n9XVUq1U0Gg0xDOK8udPpRCaTkeJnNpvFaDRCOByWS7Pb7cahQ4ckZluWhVwuh2w2K+tHWq0WarWa\nFkuUd4SHH34Y1WpV7nT8zGexgbJCALuKkvaCH2WVNO5iZwuAzFJT0shOGIuQ/Hr8xd/jDCv/LouF\nTCLpycAuIJ8T5+J417SbctmfA++29FWg5JnFVI/HA8Mw8LM/+7N378XYx6gL5R3k7NmzUtHgYaTM\naW5uTpIdtq65WJGySUq66AjJCker1RJDBh5OViALhYJ0AVjBYVXUPkDq9XoxOTmJ+fl5qbjQ2IQH\nllVMXgx5uLm0kV1Dfshw/QH3iEQiEangLC8vo16vIxaLodls7qryKMqPypNPPonXX38dLpdL1lyw\nkMCOVSwW27XbjRdPVgbt0g0AElyYkLFrxsX3dmMhBhoGLhqW+Hw+vP766yKf5CWWZ4zdPRZVGAh5\n7tmZo+yS34thGOh2u7ucMvnvrG7a5/poxa4oPwrsIlOFwUsccPu8cU6TF7Rms4nRaCTz10ycGA8p\n3WcMy2az4m7p9XqRy+WQTqcRDAbR6XTkAstkrlKpiLKDEsh6vS6yKwBi6OV2u+Hz+eB2u2X2e3Jy\nUmZ7eEFlbJuYmMDOzg7m5+cxNTWFSqUiRdWTJ09idXUV9Xpduhh2iZqi/Gd54IEHcOPGDcRiMXlf\nMXnhjkQWMChBpGqL6g3u8LUbltjn0gBInGDM4Hwcz7J9Zs0+p23/PbpPWpYl3gl2GSfjl2VZiEaj\nYhZGJ0t+LXbx+O98PLsShp8ndrfL+xmdgbtD/OZv/iYA4NFHH0U0GoVhGEgkEigUCgiFQrJPbTAY\nwDRNWXjIrhgDHCWQ3W4Xm5ubMuzZ7/dFvpHL5WTvWyaTkX0drOozUaxWq+Jod+jQIenW0e2SwZFB\njsG21WrJ3pxyuSwzOvYBV867bW1tIRAIIBwO49ChQzAMQwbaGVRN08Tq6iqq1Sq++93v4utf/zr+\n/u//fo9fsXee/TYvANybZ+2jH/0opqen8Q//8A8SjOwD2Xw/0jbZXjhgoGECxsSK3W8WT0zThNfr\nRSKRQCwWQyQSwaVLl6RAQhMFj8eDmZkZCYaxWAzPPfeczP+wgg9AZlJZ2WQXjwtQ4/G4WLC3223p\nrNkTNwZULhymMQoDHD9HfD4fHn/8cfzbv/0bPve5z+3J63Qn2W9n7V48ZxMTE1heXkYkEhE5PJ3o\n2MGmCzLncorFonTYvF4vOp2OzHWyo+Z2u6XgODc3h4WFBXg8HmSzWTQaDVy/fl06ZqVSCd1uV6r8\n4/EYL7zwAjqdDh599FEcOnQI165dk2SPMZJVfHYoIpGIxOWZmRnE43EEAgHU63WJt+VyGYPBAKVS\nCYlEAkePHsUXv/hFBAIBNJtNNJtNTE1N4aWXXpL9qalUCt/61rfQ7/dx8+bNPX7F3nn0nN15fu3X\nfg3Xrl2TxdvArVlmu9EIcHsumysy7DOjlDUytgG7Cysejwe1Wk2KmBwFiMfjUsAolUrw+XzI5/OY\nn5+X+6rH40Gn0xE5MovxlEPTrIjyRxqwtNttiUUcBQgGgxgMBjKSwGQtHo9LgkiZJg2R+Jjj8Rjr\n6+v49re/vTcv1B3k7Z4z7cDdIVwul7jFpVIpuFwusSLnJZEVB7rvsDpiH+7kgWT3jJdTOnExmLGt\nXa/XUa/X5cDY7ccph3Q4HFhaWkK73RYpJA9wNBqF1+vdVYnhRZiHLp/P7/rQoLsX2+PBYBBTU1O4\nefMmMpmMrCJIJBKyK4hV0Gg0ilQqhcceewxf+tKX9uz1Ug4up0+fRqPRQCAQ2NUtZjeLchF7dZxB\nzh7g7FIO4LYEhe6q3EUVj8dlro0zZxsbG6hWq2KlvLa2hkgkAtM0ZceOaZpSvBkMBmg2m3IGPR6P\nzA2xc0dppc/nQ7PZFAfMRqMhNs+ssvIzotFoSJePiR2NTU6cOIFwOIzr16/jtddeu/svlHKg6XQ6\nMvvFyxeLeZTwArel/CwoULrP9yP3VvHc8X3NWZ2FhQWUy2WZ85yenkYkEkGr1cLa2pqs/UgkEhgO\nh3jttdckZrbbbfknL7HpdBrValWMuHhW6EbJM2q/fNJModvtotFoIBQKYTQaYXl5GTs7O/B4PKjX\n65ibm0M2m5WRg52dHbhcLlQqFczMzGB9fX3PXi/lYEI5biKRkC4Vu9N2aSLf33zvU74MQO6LPp9P\ndsKxCwZAOl92l2M6tzKm8ExwZICxjou+/X6/JFP8uryfMtGzrxKwLGuXIozP077Ym8+xXq/D7/fv\nOq/0cmCXr1QqYWZmBqFQCF/72tfu/gu1D9AE7g7w7LPPYnNzU/ZExeNxRKNRuYTxYslFo6xoUBLJ\n5I4JHmWWrLq73W7UajU5mNQj03yh2+1KsGQ10b6Xjd0yj8eDarWKer2OYDAoHQgeQEo2LctCq9VC\nv99Ho9GQ4NvtduH1ehGJRKSlH4vF0Ov1sLm5KTvmQqGQuBLRopmdDHYDq9XqXr9sygHkk5/8pBQJ\nWF33er2yoBS4lZDZO9s8XzyHwO1OFaubNAKy73Cjlp9/jsYGXMpdLpdRLpdx+fJlCcDD4RDpdFrm\nF6LRqJwrduzYxWCSyAswuxh8LoFAQJxoaZHO789eHGo0GiJfSaVSME0T2WwWwWAQKysreOSRRzSB\nU/6boEyKu6j4fqb7HTvOLIiwkMi1AIQJEldl2Cv1vMRx3KBUKsk5ZSWexl8OhwP5fB6VSgVLS0uo\n1+uoVCrY3NxEKpWSjjv3Ynm9Xhw/fhznzp2TTgXdKk3TFHdMdhfZBaDbMqXM6XQapVJJ3GS73S6y\n2SwuXrwoRRVKMnVEQPlv5Zl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NmQZ74E6n0wBuJ5F20wj7ksdEIiHLWKlvZjKZTCYxNTWF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w8BD/qaVx7j85IEhEIhfeMb31AkEtFbb72lP/zDP7RiOZPJmH9P7PTZlStXTORme3vbmBCBwGMh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\nVg0OpZcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ec85003dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "\n",
    "plt.subplot(241)\n",
    "plt.title('T1')\n",
    "plt.axis('off')\n",
    "plt.imshow(T1[90, 0, :, :],cmap='gray')\n",
    "\n",
    "plt.subplot(242)\n",
    "plt.title('T2')\n",
    "plt.axis('off')\n",
    "plt.imshow(T2[90, 0, :, :],cmap='gray')\n",
    "    \n",
    "plt.subplot(243)\n",
    "plt.title('Flair')\n",
    "plt.axis('off')\n",
    "plt.imshow(Flair[90, 0, :, :],cmap='gray')\n",
    "\n",
    "plt.subplot(244)\n",
    "plt.title('T1c')\n",
    "plt.axis('off')\n",
    "plt.imshow(T1c[90, 0, :, :],cmap='gray')\n",
    "\n",
    "plt.subplot(245)\n",
    "plt.title('Ground Truth(Full)')\n",
    "plt.axis('off')\n",
    "plt.imshow(Label_full[90, 0, :, :],cmap='gray')\n",
    "\n",
    "plt.subplot(246)\n",
    "plt.title('Ground Truth(Core)')\n",
    "plt.axis('off')\n",
    "plt.imshow(Label_core[90, 0, :, :],cmap='gray')\n",
    "\n",
    "plt.subplot(247)\n",
    "plt.title('Ground Truth(ET)')\n",
    "plt.axis('off')\n",
    "plt.imshow(Label_ET[90, 0, :, :],cmap='gray')\n",
    "\n",
    "plt.subplot(248)\n",
    "plt.title('Ground Truth(All)')\n",
    "plt.axis('off')\n",
    "plt.imshow(Label_all[90, 0, :, :],cmap='gray')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# our U-net for full tumor segmentation\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",
    "\n",
    "\n",
    "def dice_coef_loss(y_true, y_pred):\n",
    "    return 1-dice_coef(y_true, y_pred)\n",
    "    \n",
    "def unet_model():\n",
    "    inputs = Input((2, img_size, img_size))\n",
    "    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (inputs)\n",
    "    batch1 = BatchNormalization(axis=1)(conv1)\n",
    "    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (batch1)\n",
    "    batch1 = BatchNormalization(axis=1)(conv1)\n",
    "    pool1 = MaxPooling2D((2, 2)) (batch1)\n",
    "    \n",
    "    conv2 = Conv2D(128, (3, 3), activation='relu', padding='same') (pool1)\n",
    "    batch2 = BatchNormalization(axis=1)(conv2)\n",
    "    conv2 = Conv2D(128, (3, 3), activation='relu', padding='same') (batch2)\n",
    "    batch2 = BatchNormalization(axis=1)(conv2)\n",
    "    pool2 = MaxPooling2D((2, 2)) (batch2)\n",
    "    \n",
    "    conv3 = Conv2D(256, (3, 3), activation='relu', padding='same') (pool2)\n",
    "    batch3 = BatchNormalization(axis=1)(conv3)\n",
    "    conv3 = Conv2D(256, (3, 3), activation='relu', padding='same') (batch3)\n",
    "    batch3 = BatchNormalization(axis=1)(conv3)\n",
    "    pool3 = MaxPooling2D((2, 2)) (batch3)\n",
    "    \n",
    "    conv4 = Conv2D(512, (3, 3), activation='relu', padding='same') (pool3)\n",
    "    batch4 = BatchNormalization(axis=1)(conv4)\n",
    "    conv4 = Conv2D(512, (3, 3), activation='relu', padding='same') (batch4)\n",
    "    batch4 = BatchNormalization(axis=1)(conv4)\n",
    "    pool4 = MaxPooling2D(pool_size=(2, 2)) (batch4)\n",
    "    \n",
    "    conv5 = Conv2D(1024, (3, 3), activation='relu', padding='same') (pool4)\n",
    "    batch5 = BatchNormalization(axis=1)(conv5)\n",
    "    conv5 = Conv2D(1024, (3, 3), activation='relu', padding='same') (batch5)\n",
    "    batch5 = BatchNormalization(axis=1)(conv5)\n",
    "    \n",
    "    up6 = Conv2DTranspose(512, (2, 2), strides=(2, 2), padding='same') (batch5)\n",
    "    up6 = concatenate([up6, conv4], axis=1)\n",
    "    conv6 = Conv2D(512, (3, 3), activation='relu', padding='same') (up6)\n",
    "    batch6 = BatchNormalization(axis=1)(conv6)\n",
    "    conv6 = Conv2D(512, (3, 3), activation='relu', padding='same') (batch6)\n",
    "    batch6 = BatchNormalization(axis=1)(conv6)\n",
    "    \n",
    "    up7 = Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same') (batch6)\n",
    "    up7 = concatenate([up7, conv3], axis=1)\n",
    "    conv7 = Conv2D(256, (3, 3), activation='relu', padding='same') (up7)\n",
    "    batch7 = BatchNormalization(axis=1)(conv7)\n",
    "    conv7 = Conv2D(256, (3, 3), activation='relu', padding='same') (batch7)\n",
    "    batch7 = BatchNormalization(axis=1)(conv7)\n",
    "    \n",
    "    up8 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same') (batch7)\n",
    "    up8 = concatenate([up8, conv2], axis=1)\n",
    "    conv8 = Conv2D(128, (3, 3), activation='relu', padding='same') (up8)\n",
    "    batch8 = BatchNormalization(axis=1)(conv8)\n",
    "    conv8 = Conv2D(128, (3, 3), activation='relu', padding='same') (batch8)\n",
    "    batch8 = BatchNormalization(axis=1)(conv8)\n",
    "    \n",
    "    up9 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (batch8)\n",
    "    up9 = concatenate([up9, conv1], axis=1)\n",
    "    conv9 = Conv2D(64, (3, 3), activation='relu', padding='same') (up9)\n",
    "    batch9 = BatchNormalization(axis=1)(conv9)\n",
    "    conv9 = Conv2D(64, (3, 3), activation='relu', padding='same') (batch9)\n",
    "    batch9 = BatchNormalization(axis=1)(conv9)\n",
    "\n",
    "    conv10 = Conv2D(1, (1, 1), activation='sigmoid')(batch9)\n",
    "\n",
    "    model = Model(inputs=[inputs], outputs=[conv10])\n",
    "\n",
    "    model.compile(optimizer=Adam(lr=LR), loss=dice_coef_loss, metrics=[dice_coef])\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = unet_model()\n",
    "model.load_weights('C:/brain_tumor/BRATS2018/weights-full-best.h5')\n",
    "#history = model.fit(x, y, batch_size=16, validation_split=0,validation_data = (val_x,val_y) ,epochs = 40,callbacks = callbacks_list ,verbose=1, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using Flair and T2 as input for full tumor segmentation\n",
    "x = np.zeros((1,2,240,240),np.float32)\n",
    "x[:,:1,:,:] = Flair[89:90,:,:,:]   #choosing 90th slice as example\n",
    "x[:,1:,:,:] = T2[89:90,:,:,:] \n",
    "\n",
    "pred_full = model.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MBgPC4TChUIjp6WkWFhbIZrOMjY3x3HPP8fM///PfcuyjR4/yQz/0QywsLOCc48UXX8Tv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\nJRjAERERERERTQkGcERERERERFPin7nrtOznjpvhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef1c254390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "\n",
    "plt.subplot(141)\n",
    "plt.title('T2')\n",
    "plt.axis('off')\n",
    "plt.imshow(T2[90, 0, :, :],cmap='gray')\n",
    "    \n",
    "plt.subplot(142)\n",
    "plt.title('Flair')\n",
    "plt.axis('off')\n",
    "plt.imshow(Flair[90, 0, :, :],cmap='gray')\n",
    "\n",
    "plt.subplot(143)\n",
    "plt.title('Ground Truth(full)')\n",
    "plt.axis('off')\n",
    "plt.imshow(Label_full[90, 0, :, :],cmap='gray')\n",
    "\n",
    "plt.subplot(144)\n",
    "plt.title('prediction(full)')\n",
    "plt.axis('off')\n",
    "plt.imshow(pred_full[0, 0, :, :],cmap='gray')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# cropping function\n",
    "def crop_tumor_tissue(x, pred, size):   #   input: x:T1c image , pred:prediction of full tumor ,size default  64x64\n",
    "    crop_x = []\n",
    "    list_xy = []\n",
    "    p_tmp = pred[0,:,:]\n",
    "    p_tmp[p_tmp>0.2] = 1    # threshold\n",
    "    p_tmp[p_tmp !=1] = 0\n",
    "    #get middle point from prediction of full tumor\n",
    "    index_xy = np.where(p_tmp==1)   # get all the axial of pixel which value is 1\n",
    "\n",
    "    if index_xy[0].shape[0] == 0:   #skip when no tumor\n",
    "        return [],[]\n",
    "        \n",
    "    center_x = (max(index_xy[0]) + min(index_xy[0])) / 2 \n",
    "    center_y = (max(index_xy[1]) + min(index_xy[1])) / 2 \n",
    "    \n",
    "    if center_x >= 176:\n",
    "            center_x = center_x-8\n",
    "        \n",
    "    length = max(index_xy[0]) - min(index_xy[0])\n",
    "    width = max(index_xy[1]) - min(index_xy[1])\n",
    "        \n",
    "    if width <= 64 and length <= 64:  #64x64\n",
    "        img_x = np.zeros((1,size,size),np.float32)\n",
    "        img_x[:,:,:] = x[:,int(center_x - size/2) : int(center_x + size/2),int(center_y - size/2) : int(center_y + size/2)]\n",
    "        crop_x.append(img_x)\n",
    "        #x[:,int(center_x - size/2) : int(center_x + size/2),int(center_y - size/2) : int(center_y + size/2)] = 0\n",
    "        list_xy.append((int(center_x - size/2),int(center_y - size/2)))\n",
    "            \n",
    "    if width > 64 and length <= 64:  #64x128\n",
    "        img_x = np.zeros((1,size,size),np.float32)\n",
    "        img_x[:,:,:] = x[:,int(center_x - size/2) : int(center_x + size/2),int(center_y - size) : int(center_y)]\n",
    "        crop_x.append(img_x)\n",
    "        #x[:,int(center_x - size/2) : int(center_x + size/2),int(center_y - size) : int(center_y)] = 0\n",
    "        list_xy.append((int(center_x - size/2),int(center_y - size)))\n",
    "            \n",
    "        img_x = np.zeros((1,size,size),np.float32)\n",
    "        img_x[:,:,:] = x[:,int(center_x - size/2) : int(center_x + size/2),int(center_y + 1) : int(center_y + size + 1)]\n",
    "        crop_x.append(img_x)\n",
    "        #x[:,int(center_x - size/2) : int(center_x + size/2),int(center_y) : int(center_y + size)] = 0\n",
    "        list_xy.append((int(center_x - size/2),int(center_y)))\n",
    "            \n",
    "    if width <= 64 and length > 64:  #128x64       \n",
    "        img_x = np.zeros((1,size,size),np.float32)\n",
    "        img_x[:,:,:] = x[:,int(center_x - size) : int(center_x),int(center_y - size/2) : int(center_y + size/2)]\n",
    "        crop_x.append(img_x)\n",
    "        #x[:,int(center_x - size) : int(center_x),int(center_y - size/2) : int(center_y + size/2)] = 0\n",
    "        list_xy.append((int(center_x - size),int(center_y - size/2)))\n",
    "            \n",
    "        img_x = np.zeros((1,size,size),np.float32)\n",
    "        img_x[:,:,:] = x[:,int(center_x + 1) : int(center_x + size + 1),int(center_y - size/2) : int(center_y + size/2)]\n",
    "        crop_x.append(img_x)\n",
    "        #x[:,int(center_x) : int(center_x + size),int(center_y - size/2) : int(center_y + size/2)] = 0\n",
    "        list_xy.append((int(center_x),int(center_y - size/2)))\n",
    "            \n",
    "    if width > 64 and length > 64:  #128x128\n",
    "        img_x = np.zeros((1,size,size),np.float32)\n",
    "        img_x[:,:,:] = x[:,int(center_x - size) : int(center_x),int(center_y - size) : int(center_y)]\n",
    "        crop_x.append(img_x)\n",
    "        #x[:,int(center_x - size) : int(center_x),int(center_y - size) : int(center_y)] = 0\n",
    "        list_xy.append((int(center_x - size),int(center_y - size)))\n",
    "            \n",
    "        img_x = np.zeros((1,size,size),np.float32)\n",
    "        img_x[:,:,:] = x[:,int(center_x + 1) : int(center_x + size + 1),int(center_y - size) : int(center_y)]\n",
    "        crop_x.append(img_x)\n",
    "        #x[:,int(center_x) : int(center_x + size),int(center_y - size) : int(center_y)] = 0\n",
    "        list_xy.append((int(center_x),int(center_y - size)))\n",
    "            \n",
    "        img_x = np.zeros((1,size,size),np.float32)\n",
    "        img_x[:,:,:] = x[:,int(center_x - size) : int(center_x),int(center_y + 1) : int(center_y + size + 1)]\n",
    "        crop_x.append(img_x)\n",
    "        #x[:,int(center_x - size) : int(center_x),int(center_y) : int(center_y + size)] = 0\n",
    "        list_xy.append((int(center_x - size),int(center_y)))\n",
    "            \n",
    "        img_x = np.zeros((1,size,size),np.float32)\n",
    "        img_x[:,:,:] = x[:,int(center_x + 1) : int(center_x + size + 1),int(center_y + 1) : int(center_y + size + 1)]\n",
    "        #x[:,int(center_x) : int(center_x + size),int(center_y) : int(center_y + size)] = 0\n",
    "        crop_x.append(img_x)\n",
    "        list_xy.append((int(center_x),int(center_y)))\n",
    "        \n",
    "    \n",
    "        \n",
    "    return np.array(crop_x) , list_xy   #(y,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# cropping prediction part for tumor core and enhancing tumor segmentation\n",
    "crop , li = crop_tumor_tissue(T1c[90,:,:,:],pred_full[0,:,:,:],64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crop.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# U-net for Tumor core and ET\n",
    "img_size_nec = 64\n",
    "    \n",
    "def unet_model_nec3():\n",
    "    inputs = Input((1, img_size_nec, img_size_nec))\n",
    "    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (inputs)\n",
    "    batch1 = BatchNormalization(axis=1)(conv1)\n",
    "    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (batch1)\n",
    "    batch1 = BatchNormalization(axis=1)(conv1)\n",
    "    pool1 = MaxPooling2D((2, 2)) (batch1)\n",
    "    \n",
    "    conv2 = Conv2D(128, (3, 3), activation='relu', padding='same') (pool1)\n",
    "    batch2 = BatchNormalization(axis=1)(conv2)\n",
    "    conv2 = Conv2D(128, (3, 3), activation='relu', padding='same') (batch2)\n",
    "    batch2 = BatchNormalization(axis=1)(conv2)\n",
    "    pool2 = MaxPooling2D((2, 2)) (batch2)\n",
    "    \n",
    "    conv3 = Conv2D(256, (3, 3), activation='relu', padding='same') (pool2)\n",
    "    batch3 = BatchNormalization(axis=1)(conv3)\n",
    "    conv3 = Conv2D(256, (3, 3), activation='relu', padding='same') (batch3)\n",
    "    batch3 = BatchNormalization(axis=1)(conv3)\n",
    "    pool3 = MaxPooling2D((2, 2)) (batch3)\n",
    "    \n",
    "    #conv4 = Conv2D(256, (3, 3), activation='relu', padding='same') (pool3)\n",
    "    #conv4 = Conv2D(256, (3, 3), activation='relu', padding='same') (conv4)\n",
    "    #pool4 = MaxPooling2D(pool_size=(2, 2)) (conv4)\n",
    "    \n",
    "    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same') (pool3)\n",
    "    batch5 = BatchNormalization(axis=1)(conv5)\n",
    "    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same') (batch5)\n",
    "    batch5 = BatchNormalization(axis=1)(conv5)\n",
    "    \n",
    "    #up6 = Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same') (conv5)\n",
    "    #up6 = concatenate([up6, conv4])\n",
    "    #conv6 = Conv2D(256, (3, 3), activation='relu', padding='same') (up6)\n",
    "    #conv6 = Conv2D(256, (3, 3), activation='relu', padding='same') (conv6)\n",
    "    \n",
    "    up7 = Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same') (batch5)\n",
    "    up7 = concatenate([up7, conv3], axis=1)\n",
    "    conv7 = Conv2D(256, (3, 3), activation='relu', padding='same') (up7)\n",
    "    batch7 = BatchNormalization(axis=1)(conv7)\n",
    "    conv7 = Conv2D(256, (3, 3), activation='relu', padding='same') (batch7)\n",
    "    batch7 = BatchNormalization(axis=1)(conv7)\n",
    "    \n",
    "    up8 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same') (batch7)\n",
    "    up8 = concatenate([up8, conv2], axis=1)\n",
    "    conv8 = Conv2D(128, (3, 3), activation='relu', padding='same') (up8)\n",
    "    batch8 = BatchNormalization(axis=1)(conv8)\n",
    "    conv8 = Conv2D(128, (3, 3), activation='relu', padding='same') (batch8)\n",
    "    batch8 = BatchNormalization(axis=1)(conv8)\n",
    "    \n",
    "    up9 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (batch8)\n",
    "    up9 = concatenate([up9, conv1], axis=1)\n",
    "    conv9 = Conv2D(64, (3, 3), activation='relu', padding='same') (up9)\n",
    "    batch9 = BatchNormalization(axis=1)(conv9)\n",
    "    conv9 = Conv2D(64, (3, 3), activation='relu', padding='same') (batch9)\n",
    "    batch9 = BatchNormalization(axis=1)(conv9)\n",
    "\n",
    "    conv10 = Conv2D(1, (1, 1), activation='sigmoid')(batch9)\n",
    "\n",
    "    model = Model(inputs=[inputs], outputs=[conv10])\n",
    "\n",
    "    model.compile(optimizer=Adam(lr=LR), loss=dice_coef_loss, metrics=[dice_coef])\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_core = unet_model_nec3()\n",
    "model_core.load_weights('C:/brain_tumor/BRATS2018/weights-core-best.h5')\n",
    "model_ET = unet_model_nec3()\n",
    "model_ET.load_weights('C:/brain_tumor/BRATS2018/weights-ET-best.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_core = model_core.predict(crop)\n",
    "pred_ET = model_ET.predict(crop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def paint_color_algo(pred_full, pred_core , pred_ET , li):   #input image is [n,1, y, x]\n",
    "    # first put the pred_full on T1c\n",
    "    pred_full[pred_full > 0.2] = 2      #240x240\n",
    "    pred_full[pred_full != 2] = 0\n",
    "    pred_core[pred_core > 0.2] = 1      #64x64\n",
    "    pred_core[pred_core != 1] = 0\n",
    "    pred_ET[pred_ET > 0.2] = 4          #64x64\n",
    "    pred_ET[pred_ET != 4] = 0\n",
    "\n",
    "    total = np.zeros((1,240,240),np.float32)  \n",
    "    total[:,:,:] = pred_full[:,:,:]\n",
    "    for i in range(pred_core.shape[0]):\n",
    "        for j in range(64):\n",
    "            for k in range(64):\n",
    "                if pred_core[i,0,j,k] != 0 and pred_full[0,li[i][0]+j,li[i][1]+k] !=0:\n",
    "                    total[0,li[i][0]+j,li[i][1]+k] = pred_core[i,0,j,k]\n",
    "                if pred_ET[i,0,j,k] != 0 and pred_full[0,li[i][0]+j,li[i][1]+k] !=0:\n",
    "                    total[0,li[i][0]+j,li[i][1]+k] = pred_ET[i,0,j,k]\n",
    "                \n",
    "    \n",
    "    \n",
    "    return total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = paint_color_algo(pred_full[0,:,:,:], pred_core, pred_ET, li)\n",
    "\n",
    "core = np.zeros((1,240,240),np.float32)\n",
    "ET = np.zeros((1,240,240),np.float32)\n",
    "core[:,:,:] = tmp[:,:,:]\n",
    "ET[:,:,:] = tmp[:,:,:]\n",
    "core[core == 4] = 1\n",
    "core[core != 1] = 0\n",
    "ET[ET != 4] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HAF7TZCRAmxs9x29/+9s35HV+K7ld1tpOXmd+vx+HDx/m/t1iscizFymRoes6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\nFC0AAIBm/x/bahxvrdwZtgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef548f3f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "\n",
    "plt.subplot(341)\n",
    "plt.title('T1')\n",
    "plt.axis('off')\n",
    "plt.imshow(T1[90, 0, :, :],cmap='gray')\n",
    "\n",
    "plt.subplot(342)\n",
    "plt.title('T2')\n",
    "plt.axis('off')\n",
    "plt.imshow(T2[90, 0, :, :],cmap='gray')\n",
    "    \n",
    "plt.subplot(343)\n",
    "plt.title('Flair')\n",
    "plt.axis('off')\n",
    "plt.imshow(Flair[90, 0, :, :],cmap='gray')\n",
    "\n",
    "plt.subplot(344)\n",
    "plt.title('T1c')\n",
    "plt.axis('off')\n",
    "plt.imshow(T1c[90, 0, :, :],cmap='gray')\n",
    "\n",
    "plt.subplot(345)\n",
    "plt.title('Ground Truth(Full)')\n",
    "plt.axis('off')\n",
    "plt.imshow(Label_full[90, 0, :, :],cmap='gray')\n",
    "\n",
    "plt.subplot(346)\n",
    "plt.title('Ground Truth(Core)')\n",
    "plt.axis('off')\n",
    "plt.imshow(Label_core[90, 0, :, :],cmap='gray')\n",
    "\n",
    "plt.subplot(347)\n",
    "plt.title('Ground Truth(ET)')\n",
    "plt.axis('off')\n",
    "plt.imshow(Label_ET[90, 0, :, :],cmap='gray')\n",
    "\n",
    "plt.subplot(348)\n",
    "plt.title('Ground Truth(All)')\n",
    "plt.axis('off')\n",
    "plt.imshow(Label_all[90, 0, :, :],cmap='gray')\n",
    "\n",
    "plt.subplot(349)\n",
    "plt.title('Prediction (Full)')\n",
    "plt.axis('off')\n",
    "plt.imshow(pred_full[0, 0, :, :],cmap='gray')\n",
    "\n",
    "plt.subplot(3,4,10)\n",
    "plt.title('Prediction (Core)')\n",
    "plt.axis('off')\n",
    "plt.imshow(core[0, :, :],cmap='gray')\n",
    "\n",
    "plt.subplot(3,4,11)\n",
    "plt.title('Prediction (ET)')\n",
    "plt.axis('off')\n",
    "plt.imshow(ET[0, :, :],cmap='gray')\n",
    "\n",
    "plt.subplot(3,4,12)\n",
    "plt.title('Prediction (All)')\n",
    "plt.axis('off')\n",
    "plt.imshow(tmp[0, :, :],cmap='gray')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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