Evaluation_MISA_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Import-Libraries\" data-toc-modified-id=\"Import-Libraries-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Import Libraries</a></span></li><li><span><a href=\"#Functions\" data-toc-modified-id=\"Functions-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Functions</a></span><ul class=\"toc-item\"><li><span><a href=\"#Dice-Similarity\" data-toc-modified-id=\"Dice-Similarity-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Dice Similarity</a></span></li><li><span><a href=\"#HD\" data-toc-modified-id=\"HD-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>HD</a></span></li><li><span><a href=\"#Visualization\" data-toc-modified-id=\"Visualization-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Visualization</a></span></li></ul></li><li><span><a href=\"#PreProcessing-Pipeline-1-Experiments\" data-toc-modified-id=\"PreProcessing-Pipeline-1-Experiments-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>PreProcessing Pipeline-1 Experiments</a></span><ul class=\"toc-item\"><li><span><a href=\"#FineTuned-with-1000-steps\" data-toc-modified-id=\"FineTuned-with-1000-steps-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>FineTuned with 1000 steps</a></span></li><li><span><a href=\"#Neuronet-FineTune-With-1000-step+5000-Steps,400-Samples-No-preprocessing\" data-toc-modified-id=\"Neuronet-FineTune-With-1000-step+5000-Steps,400-Samples-No-preprocessing-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>Neuronet FineTune With 1000 step+5000 Steps,400 Samples No preprocessing</a></span></li></ul></li><li><span><a href=\"#Preprocessing:-Histogram-Matching-+-Normalization\" data-toc-modified-id=\"Preprocessing:-Histogram-Matching-+-Normalization-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Preprocessing: Histogram Matching + Normalization</a></span><ul class=\"toc-item\"><li><span><a href=\"#1000-steps-200-samples\" data-toc-modified-id=\"1000-steps-200-samples-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>1000 steps 200 samples</a></span></li><li><span><a href=\"#1000,-50-samples\" data-toc-modified-id=\"1000,-50-samples-4.2\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span>1000, 50 samples</a></span></li><li><span><a href=\"#1000+2000-steps,-50-samples-preprocessing.\" data-toc-modified-id=\"1000+2000-steps,-50-samples-preprocessing.-4.3\"><span class=\"toc-item-num\">4.3&nbsp;&nbsp;</span>1000+2000 steps, 50 samples preprocessing.</a></span></li><li><span><a href=\"#FineTuned-1000+2000+2000-steps,-50-samples-preprocessing.\" data-toc-modified-id=\"FineTuned-1000+2000+2000-steps,-50-samples-preprocessing.-4.4\"><span class=\"toc-item-num\">4.4&nbsp;&nbsp;</span>FineTuned 1000+2000+2000 steps, 50 samples preprocessing.</a></span></li><li><span><a href=\"#FineTuned-1000+2000+2000-steps,-50-samples-preprocessing+1000-steps-100-samples\" data-toc-modified-id=\"FineTuned-1000+2000+2000-steps,-50-samples-preprocessing+1000-steps-100-samples-4.5\"><span class=\"toc-item-num\">4.5&nbsp;&nbsp;</span>FineTuned 1000+2000+2000 steps, 50 samples preprocessing+1000 steps 100 samples</a></span></li><li><span><a href=\"#FineTuned-20000-steps,-50-sample,-model-364\" data-toc-modified-id=\"FineTuned-20000-steps,-50-sample,-model-364-4.6\"><span class=\"toc-item-num\">4.6&nbsp;&nbsp;</span>FineTuned 20000 steps, 50 sample, model 364</a></span></li></ul></li><li><span><a href=\"#PreProcessing-Pipeline-2-Experiments\" data-toc-modified-id=\"PreProcessing-Pipeline-2-Experiments-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>PreProcessing Pipeline-2 Experiments</a></span><ul class=\"toc-item\"><li><span><a href=\"#Pretrained-with-50-samples-2000step\" data-toc-modified-id=\"Pretrained-with-50-samples-2000step-5.1\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span>Pretrained with 50 samples 2000step</a></span></li><li><span><a href=\"#Pretrained-with-50-samples-2000step+2000step...model-Here-is-the-model-Name-in-the-folder-1546123949,-saved-in-the-destination-folder\" data-toc-modified-id=\"Pretrained-with-50-samples-2000step+2000step...model-Here-is-the-model-Name-in-the-folder-1546123949,-saved-in-the-destination-folder-5.2\"><span class=\"toc-item-num\">5.2&nbsp;&nbsp;</span>Pretrained with 50 samples 2000step+2000step...model Here is the model Name in the folder 1546123949, saved in the destination folder</a></span></li><li><span><a href=\"#samples-50-with-additional-training-**Best-Model-&gt;&gt;Folder--Folder-name-1546148883\" data-toc-modified-id=\"samples-50-with-additional-training-**Best-Model->>Folder--Folder-name-1546148883-5.3\"><span class=\"toc-item-num\">5.3&nbsp;&nbsp;</span>samples 50 with additional training <em>**</em>Best Model &gt;&gt;Folder  Folder name 1546148883</a></span></li></ul></li><li><span><a href=\"#Balanc-Sampling\" data-toc-modified-id=\"Balanc-Sampling-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Balanc Sampling</a></span><ul class=\"toc-item\"><li><span><a href=\"#Using-Pretrained-Model-For-Extraced_Balanced-Sample.50-samples.4000-steps\" data-toc-modified-id=\"Using-Pretrained-Model-For-Extraced_Balanced-Sample.50-samples.4000-steps-6.1\"><span class=\"toc-item-num\">6.1&nbsp;&nbsp;</span>Using Pretrained Model For Extraced_Balanced Sample.50 samples.4000 steps</a></span></li><li><span><a href=\"#training_fromScratech_4000step_classbalanced-training\" data-toc-modified-id=\"training_fromScratech_4000step_classbalanced-training-6.2\"><span class=\"toc-item-num\">6.2&nbsp;&nbsp;</span>training_fromScratech_4000step_classbalanced training</a></span></li><li><span><a href=\"#training_fromScratech_4000step_class-random-50-samplestraining\" data-toc-modified-id=\"training_fromScratech_4000step_class-random-50-samplestraining-6.3\"><span class=\"toc-item-num\">6.3&nbsp;&nbsp;</span>training_fromScratech_4000step_class random 50 samplestraining</a></span></li></ul></li><li><span><a href=\"#different-Patch-Size\" data-toc-modified-id=\"different-Patch-Size-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>different Patch Size</a></span><ul class=\"toc-item\"><li><span><a href=\"#32-patch-size-200-samples-4000-steps\" data-toc-modified-id=\"32-patch-size-200-samples-4000-steps-7.1\"><span class=\"toc-item-num\">7.1&nbsp;&nbsp;</span>32 patch size 200 samples 4000 steps</a></span></li><li><span><a href=\"#64-patch-size-200-samples-4000-steps\" data-toc-modified-id=\"64-patch-size-200-samples-4000-steps-7.2\"><span class=\"toc-item-num\">7.2&nbsp;&nbsp;</span>64 patch size 200 samples 4000 steps</a></span></li><li><span><a href=\"#128-patch\" data-toc-modified-id=\"128-patch-7.3\"><span class=\"toc-item-num\">7.3&nbsp;&nbsp;</span>128 patch</a></span></li></ul></li><li><span><a href=\"#Ami\" data-toc-modified-id=\"Ami-8\"><span class=\"toc-item-num\">8&nbsp;&nbsp;</span>Ami</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import nibabel as nib\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from numpy.linalg import inv, det, norm\n",
    "from math import sqrt, pi\n",
    "from functools import partial\n",
    "from scipy.spatial.distance import dice\n",
    "import time\n",
    "import operator\n",
    "import matplotlib.pyplot as plt\n",
    "import SimpleITK as sitk\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from scipy.ndimage import label\n",
    "from scipy.ndimage import labeled_comprehension as lc\n",
    "from scipy.ndimage.morphology import binary_erosion as imerode\n",
    "from sklearn.neighbors import NearestNeighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Functions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Dice Similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def dice_similarity(Seg_img, GT_img,state):\n",
    "    \"\"\"   \n",
    "    Inputs:\n",
    "        Seg_img (numpy.ndarray): Segmented Image.\n",
    "        GT_img (numpy.ndarray): Ground Truth Image.\n",
    "        State: \"nifti\" if the images are nifti file\n",
    "               \"arr\"   if the images are an ndarray\n",
    "    output:\n",
    "        Dice Similarity Coefficient: dice_CSF, dice_GM, dice_WM.\"\"\"\n",
    "    \n",
    "    if (state==\"nifti\"):\n",
    "       segmented_data = Seg_img.get_data().copy()\n",
    "       groundtruth_data = GT_img.get_data().copy()\n",
    "    elif (state==\"arr\"):\n",
    "       segmented_data = Seg_img.copy()\n",
    "       groundtruth_data = GT_img.copy()\n",
    "    \n",
    "    #Calculte DICE\n",
    "    def dice_coefficient(SI,GT):\n",
    "        #   2 * TP / (FN + (2 * TP) + FP)\n",
    "        intersection = np.logical_and(SI, GT)\n",
    "        return 2. * intersection.sum() / (SI.sum() + GT.sum())\n",
    "    \n",
    "    #Dice  for CSF\n",
    "    Seg_CSF = (segmented_data == 1) * 1\n",
    "    GT_CSF = (groundtruth_data == 1) * 1\n",
    "    dice_CSF = dice_coefficient(Seg_CSF, GT_CSF)\n",
    "    #Dice  for GM\n",
    "    Seg_GM = (segmented_data == 2) * 1\n",
    "    GT_GM = (groundtruth_data == 2) * 1\n",
    "    dice_GM = dice_coefficient(Seg_GM, GT_GM)\n",
    "    #Dice  for WM\n",
    "    Seg_WM = (segmented_data == 3) * 1\n",
    "    GT_WM = (groundtruth_data == 3) * 1\n",
    "    dice_WM = dice_coefficient(Seg_WM, GT_WM)\n",
    "    \n",
    "    return dice_CSF, dice_GM, dice_WM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## HD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def Haursdoff_distance(Seg_img, GT_img,state):\n",
    "    \"\"\"   \n",
    "    Inputs:\n",
    "        Seg_img (numpy.ndarray): Segmented Image.\n",
    "        GT_img (numpy.ndarray): Ground Truth Image.\n",
    "        State: \"nifti\" if the images are nifti file\n",
    "               \"arr\"   if the images are an ndarray\n",
    "    output:\n",
    "        Dice Similarity Coefficient: dice_CSF, dice_GM, dice_WM.\"\"\"\n",
    "    \n",
    "    if (state==\"nifti\"):\n",
    "       segmented_data = Seg_img.get_data().copy()\n",
    "       groundtruth_data = GT_img.get_data().copy()\n",
    "    elif (state==\"arr\"):\n",
    "       segmented_data = Seg_img.copy()\n",
    "       groundtruth_data = GT_img.copy()\n",
    "    \n",
    "    ###ecludian_distance\n",
    "    def eucl_distance(a, b):\n",
    "        \"\"\"\n",
    "        Euclidian distance between Region a and b\n",
    "        Inputs:\n",
    "        - a: 3D np.ndarray\n",
    "        - b: 3D np.ndarray\n",
    "        Output:\n",
    "        - (float) Euclidian distances between A and B\n",
    "        \"\"\"\n",
    "        nbrs_a = NearestNeighbors(n_neighbors=1,\n",
    "                                  algorithm='kd_tree').fit(a) if a.size > 0 else None\n",
    "        nbrs_b = NearestNeighbors(n_neighbors=1,\n",
    "                                  algorithm='kd_tree').fit(b) if b.size > 0 else None\n",
    "        distances_a, _ = nbrs_a.kneighbors(b) if nbrs_a and b.size > 0 else ([np.inf], None)\n",
    "        distances_b, _ = nbrs_b.kneighbors(a) if nbrs_b and a.size > 0 else ([np.inf], None)\n",
    "\n",
    "        return [distances_a, distances_b]\n",
    "    \n",
    "    \n",
    "    ####as logical\n",
    "    def as_logical(mask):\n",
    "        \"\"\"\n",
    "        convert to Boolean\n",
    "        Inputs:\n",
    "        - mask: 3D np.ndarray, input MRI mask\n",
    "        Output:\n",
    "        - Same mask binarized\n",
    "        \"\"\"\n",
    "        return np.array(mask).astype(dtype=np.bool)\n",
    "    \n",
    "    ####Surface Distance\n",
    "    def surface_distance(gt, mask, spacing=list((1, 1, 1))):\n",
    "        \"\"\"\n",
    "        Compute the surface distance between the input mask and a\n",
    "        ground truth mask\n",
    "        Inputs:\n",
    "        - gt: 3D np.ndarray, reference image (ground truth)\n",
    "        - mask: 3D np.ndarray, input MRI mask\n",
    "        - spacing: sets the input resolution (def: (1, 1, 1))\n",
    "        Output:\n",
    "        - (float) Euclidian distance between gt and mask\n",
    "        \"\"\"\n",
    "        a = as_logical(gt)\n",
    "        b = as_logical(mask)\n",
    "        a_bound = np.stack(np.where(\n",
    "            np.logical_and(a, np.logical_not(imerode(a)))), axis=1) * spacing\n",
    "        b_bound = np.stack(np.where(\n",
    "            np.logical_and(b, np.logical_not(imerode(b)))), axis=1) * spacing\n",
    "        return eucl_distance(a_bound, b_bound)\n",
    "    \n",
    "    def HD(gt, mask, spacing=(1, 1, 1)):\n",
    "        \"\"\"\n",
    "        Compute the Haursdoff distance between an input mask and a\n",
    "        groud truth mask\n",
    "        Inputs:\n",
    "        - gt: 3D np.ndarray, reference image (ground truth)\n",
    "        - mask: 3D np.ndarray, input MRI mask\n",
    "        - spacing: sets the input resolution (def: (1, 1, 1))\n",
    "        Output:\n",
    "        - (float) Haursdoff distance betwwen Gt and mask\n",
    "        - spacing: sets the input resolution\n",
    "        \"\"\"\n",
    "        distances = surface_distance(gt, mask, spacing)\n",
    "        return np.max([np.max(distances[0]), np.max(distances[1])])\n",
    "    \n",
    "    \n",
    "    #Dice  for CSF\n",
    "    Seg_CSF = (segmented_data == 1) * 1\n",
    "    GT_CSF = (groundtruth_data == 1) * 1\n",
    "    HD_CSF = HD(Seg_CSF, GT_CSF)\n",
    "    #Dice  for GM\n",
    "    Seg_GM = (segmented_data == 2) * 1\n",
    "    GT_GM = (groundtruth_data == 2) * 1\n",
    "    HD_GM = HD(Seg_GM, GT_GM)\n",
    "    #Dice  for WM\n",
    "    Seg_WM = (segmented_data == 3) * 1\n",
    "    GT_WM = (groundtruth_data == 3) * 1\n",
    "    HD_WM = HD(Seg_WM, GT_WM)\n",
    "    \n",
    "    return HD_CSF, HD_GM, HD_WM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Visualization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def visualize_dices(groudtruth_path, Segmented_data_path):\n",
    "###\n",
    "#calculates dices and visualize as box plots\n",
    "#This function takes two paths and shows the box plots for three regions (CSF, White matter, Gray matter)\n",
    "#Parameters:\n",
    "# groundtruth_path: string\n",
    "#    the absolute path where ground truth images are located\n",
    "#Segmented_data_path: string\n",
    "#    the absolute path where the segmented images are located\n",
    "#Returns:\n",
    "# average_dices: a float array (number of regions X 1)\n",
    "#     the average dices for each region\n",
    "\n",
    "####Loading The Folder\n",
    "\n",
    "##Segmented_data_path =\"H:/MISA_results/Neuronet_Pretrained_Plus_TRaining/val/\"\n",
    "    Image_list=os.listdir(Segmented_data_path)\n",
    "    GT_list=os.listdir(groudtruth_path)\n",
    "\n",
    "    CSF=0\n",
    "    GM=0\n",
    "    WM=0\n",
    "\n",
    "    CSF_List=[]\n",
    "    GM_List=[]\n",
    "    WM_List=[]\n",
    "\n",
    "\n",
    "    for i in range(0,5):\n",
    "        print(\"Print the Dice for Validation Image: \",Image_list[i])\n",
    "\n",
    "        #Segmentation\n",
    "        prediction = sitk.ReadImage(Segmented_data_path+Image_list[i],sitk.sitkInt8) \n",
    "        prediction_img=sitk.GetArrayFromImage(prediction)\n",
    "\n",
    "        #Groud Truth\n",
    "        GT = sitk.ReadImage(groudtruth_path + GT_list[i],sitk.sitkInt8)\n",
    "        GT_img=sitk.GetArrayFromImage(GT)\n",
    "\n",
    "        ###CALCLATING THE Dice\n",
    "        dice_CSF, dice_GM, dice_WM = dice_similarity(prediction_img,GT_img,\"arr\")\n",
    "        print(\"CSF DICE = {}\".format(dice_CSF), \"GM DICE = {}\".format(dice_GM), \"WM DICE = {}\".format(dice_WM))\n",
    "\n",
    "        ###\n",
    "        CSF_List.append(dice_CSF)\n",
    "        GM_List.append(dice_GM)\n",
    "        WM_List.append(dice_WM)\n",
    "\n",
    "        ####\n",
    "\n",
    "    df=pd.DataFrame(list(zip(CSF_List,GM_List,WM_List)),\n",
    "    columns=['CSF','GM','WM'])\n",
    "    \n",
    "    Average_CSF=df['CSF'].mean()\n",
    "    Average_GM=df['GM'].mean()\n",
    "    Average_WM=df['WM'].mean()\n",
    "    \n",
    "    Std_CSF=df['CSF'].std()\n",
    "    Std_GM=df['GM'].std()\n",
    "    Std_WM=df['WM'].std()\n",
    "    \n",
    "    #print(\"Avg_CSF DICE = {} ± {}\".format(Average_CSF,Std_CSF), \"Avg_GM DICE = {}\".format(Average_GM), \"Avg_WM DICE = {}\".format(Average_WM))\n",
    "    \n",
    "    \n",
    "    print(\"###Printing Avg ± Standard Daviation###\")\n",
    "    print(\"Avg CSF DICE = {} ± {}\".format(Average_CSF,Std_CSF))\n",
    "    print(\"Avg GM DICE = {} ± {}\".format(Average_GM,Std_GM))\n",
    "    print(\"Avg WM DICE = {} ± {}\".format(Average_WM,Std_WM))\n",
    "\n",
    "    #boxplot = df.boxplot(column=['CSF','GM','WM'])\n",
    "\n",
    "    boxplot=sns.boxplot(data=df)\n",
    "    \n",
    "    boxplot.set(xlabel='Tissue Type', ylabel='DSC',title='DSC Boxplot')\n",
    "    \n",
    "    plt.savefig('foo.png', bbox_inches='tight')\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "    return np.array([Average_CSF, Average_GM, Average_WM])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# PreProcessing Pipeline-1 Experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## FineTuned with 1000 steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Here We Used the Pretrained Model Of Neuronet and We trained it for 1000 step more (Pretrained Model Was tranined with 100000 step already on Biobank data). No Preprocessing is benn used so far.\n",
    "\n",
    "Neuronet FineTuned with 1000 steps, It's A 3D rasidual encoder FCN decoder Model\n",
    "\n",
    "* Patch Size: 128x128X128\n",
    "* Number of Patches: 200\n",
    "* Preprocessing: PreProcessing Pipeline-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the Dice for Validation Image:  IBSR_11_seg.nii.gz\n",
      "CSF DICE = 0.8497201827796889 GM DICE = 0.9052246545838913 WM DICE = 0.9154533696918394\n",
      "Print the Dice for Validation Image:  IBSR_12_seg.nii.gz\n",
      "CSF DICE = 0.8428270395022912 GM DICE = 0.8960637987780202 WM DICE = 0.8879498261176352\n",
      "Print the Dice for Validation Image:  IBSR_13_seg.nii.gz\n",
      "CSF DICE = 0.2755500078015291 GM DICE = 0.9014070842127316 WM DICE = 0.8737110823519539\n",
      "Print the Dice for Validation Image:  IBSR_14_seg.nii.gz\n",
      "CSF DICE = 0.1610079575596817 GM DICE = 0.9278953098798151 WM DICE = 0.9327289562652081\n",
      "Print the Dice for Validation Image:  IBSR_17_seg.nii.gz\n",
      "CSF DICE = 0.0 GM DICE = 0.8913491665565545 WM DICE = 0.7696295521685708\n",
      "###Printing Avg ± Standard Daviation###\n",
      "Avg CSF DICE = 0.42582103752863826 ± 0.39611090515361924\n",
      "Avg GM DICE = 0.9043880028022026 ± 0.014155237291891876\n",
      "Avg WM DICE = 0.8758945573190415 ± 0.06371302246849947\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.42582104, 0.904388  , 0.87589456])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "####Loading The Folder\n",
    "GT_data_path =\"H:/MISA_results/val_gt/\"    \n",
    "Segmented_data_path =\"H:/MISA_results/Neuronet_Pretrained_Plus_TRaining/FineTune_1000_Steps_No_Preprocessing/val/\"\n",
    "\n",
    "visualize_dices(Segmented_data_path,GT_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Neuronet FineTune With 1000 step+5000 Steps,400 Samples No preprocessing "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Here We Used the Pretrained Model Of Neuronet and We trained it for 1000 step more (Pretrained Model Was tranined with 100000 step already on Biobank data). No Preprocessing is benn used so far.\n",
    "\n",
    "Neuronet FineTuned with 1000 steps, It's A 3D rasidual Unet Model\n",
    "\n",
    "Patch Size: 128x128X128\n",
    "Number of Patches: 400\n",
    "Preprocessing:pipeline-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the Dice for Validation Image:  IBSR_11_seg.nii.gz\n",
      "CSF DICE = 0.6884880340859316 GM DICE = 0.8585491219855336 WM DICE = 0.8959564349097198\n",
      "Print the Dice for Validation Image:  IBSR_12_seg.nii.gz\n",
      "CSF DICE = 0.6587600781486476 GM DICE = 0.8329452000855018 WM DICE = 0.8568883973309217\n",
      "Print the Dice for Validation Image:  IBSR_13_seg.nii.gz\n",
      "CSF DICE = 0.8528349149766367 GM DICE = 0.9089110150735037 WM DICE = 0.8717713625430956\n",
      "Print the Dice for Validation Image:  IBSR_14_seg.nii.gz\n",
      "CSF DICE = 0.9104515939689644 GM DICE = 0.943800253484689 WM DICE = 0.9383074225998761\n",
      "Print the Dice for Validation Image:  IBSR_17_seg.nii.gz\n",
      "CSF DICE = 0.8804566674779299 GM DICE = 0.9232974028364777 WM DICE = 0.8725968185244968\n",
      "###Printing Avg ± Standard Daviation###\n",
      "Avg CSF DICE = 0.798198257731622 ± 0.11600836271915461\n",
      "Avg GM DICE = 0.8935005986931411 ± 0.04621673876122674\n",
      "Avg WM DICE = 0.887104087181622 ± 0.03185391192670842\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.79819826, 0.8935006 , 0.88710409])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###Loading The Folder\n",
    "GT_data_path =\"H:/MISA_results/val_gt/\"    \n",
    "Segmented_data_path =\"H:/MISA_results/Neuronet_Pretrained_Plus_TRaining/FineTune_1000_StepsPlus4000Steps_No_Preprocessing/val/\"\n",
    "\n",
    "visualize_dices(Segmented_data_path,GT_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Preprocessing: Histogram Matching + Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## 1000 steps 200 samples "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Here We Used the Pretrained Model Of Neuronet and We trained it for 1000 step more (Pretrained Model Was tranined with 100000 step already on Biobank data). No Preprocessing is benn used so far.\n",
    "\n",
    "Neuronet FineTuned with 1000 steps, It's A 3D rasidual Unet Model\n",
    "\n",
    "* Patch Size: 128x128X128\n",
    "* Number of Patches: 200\n",
    "* Preprocessing: Histogram Matching + Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the Dice for Validation Image:  IBSR_11_seg.nii.gz\n",
      "CSF DICE = 0.8436647975718916 GM DICE = 0.9040520726221559 WM DICE = 0.9136144288241513\n",
      "Print the Dice for Validation Image:  IBSR_12_seg.nii.gz\n",
      "CSF DICE = 0.7818147801683817 GM DICE = 0.839099824399679 WM DICE = 0.8268842302742087\n",
      "Print the Dice for Validation Image:  IBSR_13_seg.nii.gz\n",
      "CSF DICE = 0.7070498479894453 GM DICE = 0.8635869091176187 WM DICE = 0.8113461016127966\n",
      "Print the Dice for Validation Image:  IBSR_14_seg.nii.gz\n",
      "CSF DICE = 0.44423356034386513 GM DICE = 0.9289308882356134 WM DICE = 0.9196696385016969\n",
      "Print the Dice for Validation Image:  IBSR_17_seg.nii.gz\n",
      "CSF DICE = 0.0 GM DICE = 0.8580801387052231 WM DICE = 0.8076628278261249\n",
      "###Printing Avg ± Standard Daviation###\n",
      "Avg CSF DICE = 0.5553525972147167 ± 0.3457546271070652\n",
      "Avg GM DICE = 0.8787499666160581 ± 0.0367001884847863\n",
      "Avg WM DICE = 0.8558354454077957 ± 0.05601627171248487\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.5553526 , 0.87874997, 0.85583545])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "####Loading The Folder\n",
    "GT_data_path =\"H:/MISA_results/val_gt/\"    \n",
    "Segmented_data_path =\"H:/MISA_results/Neuronet_Pretrained_Plus_TRaining/FineTune_1000_Steps_Preprocessing/val/\"\n",
    "\n",
    "visualize_dices(Segmented_data_path,GT_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## 1000, 50 samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the Dice for Validation Image:  IBSR_11_seg.nii.gz\n",
      "CSF DICE = 0.8260207853198244 GM DICE = 0.8974430091650317 WM DICE = 0.907335441733089\n",
      "Print the Dice for Validation Image:  IBSR_12_seg.nii.gz\n",
      "CSF DICE = 0.7588652482269503 GM DICE = 0.8290838336536573 WM DICE = 0.813474499115328\n",
      "Print the Dice for Validation Image:  IBSR_13_seg.nii.gz\n",
      "CSF DICE = 0.8421460558043403 GM DICE = 0.8608663945342797 WM DICE = 0.8115309759192177\n",
      "Print the Dice for Validation Image:  IBSR_14_seg.nii.gz\n",
      "CSF DICE = 0.8884640333208152 GM DICE = 0.9299760180998886 WM DICE = 0.9232044553375508\n",
      "Print the Dice for Validation Image:  IBSR_17_seg.nii.gz\n",
      "CSF DICE = 0.0796415742922251 GM DICE = 0.8844241938958416 WM DICE = 0.8774308833445719\n",
      "###Printing Avg ± Standard Daviation###\n",
      "Avg CSF DICE = 0.6790275393928311 ± 0.3382735227019543\n",
      "Avg GM DICE = 0.8803586898697399 ± 0.038010991853978195\n",
      "Avg WM DICE = 0.8665952510899515 ± 0.05204724958143977\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.67902754, 0.88035869, 0.86659525])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GT_data_path =\"H:/MISA_results/val_gt/\"    \n",
    "Segmented_data_path =\"H:/MISA_results/Neuronet_Pretrained_Plus_TRaining/FineTuned_1000steps_50 samplespreprocessing/val/\"\n",
    "\n",
    "visualize_dices(Segmented_data_path,GT_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## 1000+2000 steps, 50 samples preprocessing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the Dice for Validation Image:  IBSR_11_seg.nii.gz\n",
      "CSF DICE = 0.7942126129071811 GM DICE = 0.9157964237801487 WM DICE = 0.9257583438327375\n",
      "Print the Dice for Validation Image:  IBSR_12_seg.nii.gz\n",
      "CSF DICE = 0.7111843481899979 GM DICE = 0.8599143506158184 WM DICE = 0.8432936119517357\n",
      "Print the Dice for Validation Image:  IBSR_13_seg.nii.gz\n",
      "CSF DICE = 0.8019398044800247 GM DICE = 0.8884664838701412 WM DICE = 0.8578094088663821\n",
      "Print the Dice for Validation Image:  IBSR_14_seg.nii.gz\n",
      "CSF DICE = 0.8633253073121278 GM DICE = 0.941555680766645 WM DICE = 0.939839698765686\n",
      "Print the Dice for Validation Image:  IBSR_17_seg.nii.gz\n",
      "CSF DICE = 0.601391982182628 GM DICE = 0.8605809553131343 WM DICE = 0.7867645921917279\n",
      "###Printing Avg ± Standard Daviation###\n",
      "Avg CSF DICE = 0.7544108110143919 ± 0.10122851034035803\n",
      "Avg GM DICE = 0.8932627788691775 ± 0.03550772180205214\n",
      "Avg WM DICE = 0.8706931311216538 ± 0.0627975896525364\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.75441081, 0.89326278, 0.87069313])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GT_data_path =\"H:/MISA_results/val_gt/\"    \n",
    "Segmented_data_path =\"H:/MISA_results/Neuronet_Pretrained_Plus_TRaining/FineTuned1000_2000steps50samplespreprocessing/val/\"\n",
    "\n",
    "visualize_dices(Segmented_data_path,GT_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## FineTuned 1000+2000+2000 steps, 50 samples preprocessing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the Dice for Validation Image:  IBSR_11_seg.nii.gz\n",
      "CSF DICE = 0.8358074673864148 GM DICE = 0.9270294499668131 WM DICE = 0.9337678418352768\n",
      "Print the Dice for Validation Image:  IBSR_12_seg.nii.gz\n",
      "CSF DICE = 0.7711306256860593 GM DICE = 0.8777429048668686 WM DICE = 0.8628300112393055\n",
      "Print the Dice for Validation Image:  IBSR_13_seg.nii.gz\n",
      "CSF DICE = 0.8398905094578584 GM DICE = 0.9065874706081436 WM DICE = 0.8769575086716996\n",
      "Print the Dice for Validation Image:  IBSR_14_seg.nii.gz\n",
      "CSF DICE = 0.8829140803658935 GM DICE = 0.9468597571086033 WM DICE = 0.9383978222180333\n",
      "Print the Dice for Validation Image:  IBSR_17_seg.nii.gz\n",
      "CSF DICE = 0.5557581546120228 GM DICE = 0.8554168159638911 WM DICE = 0.7280339033537783\n",
      "###Printing Avg ± Standard Daviation###\n",
      "Avg CSF DICE = 0.7771001675016497 ± 0.13001315352484433\n",
      "Avg GM DICE = 0.902727279702864 ± 0.03679562161614292\n",
      "Avg WM DICE = 0.8679974174636188 ± 0.0851157099411341\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.77710017, 0.90272728, 0.86799742])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GT_data_path =\"H:/MISA_results/val_gt/\"    \n",
    "Segmented_data_path =\"H:/MISA_results/Neuronet_Pretrained_Plus_TRaining/FineTuned1000_2000_2000steps50samplespreprocessing/val/\"\n",
    "\n",
    "visualize_dices(Segmented_data_path,GT_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## FineTuned 1000+2000+2000 steps, 50 samples preprocessing+1000 steps 100 samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the Dice for Validation Image:  IBSR_11_seg.nii.gz\n",
      "CSF DICE = 0.8836188385742234 GM DICE = 0.9389339784990169 WM DICE = 0.9463049027145953\n",
      "Print the Dice for Validation Image:  IBSR_12_seg.nii.gz\n",
      "CSF DICE = 0.8721128723218952 GM DICE = 0.8906359748187679 WM DICE = 0.8777939672009176\n",
      "Print the Dice for Validation Image:  IBSR_13_seg.nii.gz\n",
      "CSF DICE = 0.8822541857616044 GM DICE = 0.916799443974533 WM DICE = 0.8846064576026408\n",
      "Print the Dice for Validation Image:  IBSR_14_seg.nii.gz\n",
      "CSF DICE = 0.9094240475696606 GM DICE = 0.9481432827484801 WM DICE = 0.9352539840915045\n",
      "Print the Dice for Validation Image:  IBSR_17_seg.nii.gz\n",
      "CSF DICE = 0.0036154742297031294 GM DICE = 0.8299199998521767 WM DICE = 0.6436794631574361\n",
      "###Printing Avg ± Standard Daviation###\n",
      "Avg CSF DICE = 0.7102050836914173 ± 0.39523550817610686\n",
      "Avg GM DICE = 0.9048865359785949 ± 0.04742191448154177\n",
      "Avg WM DICE = 0.8575277549534188 ± 0.1232860657786611\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.71020508, 0.90488654, 0.85752775])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GT_data_path =\"H:/MISA_results/val_gt/\"    \n",
    "Segmented_data_path =\"H:/MISA_results/Neuronet_Pretrained_Plus_TRaining/60067/\"\n",
    "\n",
    "visualize_dices(Segmented_data_path,GT_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## FineTuned 20000 steps, 50 sample, model 364 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the Dice for Validation Image:  IBSR_11_seg.nii.gz\n",
      "CSF DICE = 0.8753492475859026 GM DICE = 0.921031658113626 WM DICE = 0.928391623658333\n",
      "Print the Dice for Validation Image:  IBSR_12_seg.nii.gz\n",
      "CSF DICE = 0.8767614471655492 GM DICE = 0.862074014207823 WM DICE = 0.8434345532339135\n",
      "Print the Dice for Validation Image:  IBSR_13_seg.nii.gz\n",
      "CSF DICE = 0.8943592502914537 GM DICE = 0.900456363923419 WM DICE = 0.8612796021287968\n",
      "Print the Dice for Validation Image:  IBSR_14_seg.nii.gz\n",
      "CSF DICE = 0.9075229850345614 GM DICE = 0.9527689325311053 WM DICE = 0.9476497981495483\n",
      "Print the Dice for Validation Image:  IBSR_17_seg.nii.gz\n",
      "CSF DICE = 0.4074488133644233 GM DICE = 0.8860524474934314 WM DICE = 0.7825393061918565\n",
      "###Printing Avg ± Standard Daviation###\n",
      "Avg CSF DICE = 0.792288348688378 ± 0.2155422090961872\n",
      "Avg GM DICE = 0.9044766832538809 ± 0.034495689245054216\n",
      "Avg WM DICE = 0.8726589766724896 ± 0.06677361631608975\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.79228835, 0.90447668, 0.87265898])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GT_data_path =\"H:/MISA_results/val_gt/\"    \n",
    "Segmented_data_path =\"H:/MISA_results/Neuronet_Pretrained_Plus_TRaining/Finetued_27000total/val/\"\n",
    "\n",
    "visualize_dices(Segmented_data_path,GT_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# PreProcessing Pipeline-2 Experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Pretrained with 50 samples 2000step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the Dice for Validation Image:  IBSR_11_seg.nii.gz\n",
      "CSF DICE = 0.8649492583918813 GM DICE = 0.9373020491150577 WM DICE = 0.9427661430133366\n",
      "Print the Dice for Validation Image:  IBSR_12_seg.nii.gz\n",
      "CSF DICE = 0.8878021517395557 GM DICE = 0.931658212299706 WM DICE = 0.9376831921844049\n",
      "Print the Dice for Validation Image:  IBSR_13_seg.nii.gz\n",
      "CSF DICE = 0.875902957612103 GM DICE = 0.9313647631649846 WM DICE = 0.9054247339083923\n",
      "Print the Dice for Validation Image:  IBSR_14_seg.nii.gz\n",
      "CSF DICE = 0.9053660074068237 GM DICE = 0.9462487590412707 WM DICE = 0.9362693799671344\n",
      "Print the Dice for Validation Image:  IBSR_17_seg.nii.gz\n",
      "CSF DICE = 0.9128559758344891 GM DICE = 0.9415902894441656 WM DICE = 0.927293624390279\n",
      "###Printing Avg ± Standard Daviation###\n",
      "Avg CSF DICE = 0.8893752701969706 ± 0.019922695440867028\n",
      "Avg GM DICE = 0.9376328146130367 ± 0.006422418800615006\n",
      "Avg WM DICE = 0.9298874146927094 ± 0.014769188958003908\n"
     ]
    },
    {
     "data": {
      "image/png": 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EziuXzwWOlDRxsAeQdImkbkndvb29z6vYiIjYtzrDQv20tU74fQUwXdIKYDrwKLBzsAewfaPtLttdHR0d+19pREQMqM7nLBrAsU3rncDG5g62NwLvAJA0ATjP9pYaa4qIiP0w4MhC0hRJp/fTfoakl1fsezlwkqQTJY0DzgcWt+xnkqS+Gq4Cbhp86RERMVyqTkN9jv4vMm8rt+2T7Z3ApcBS4AFgke3VkuZJOqfsNgNYK+lB4Gjgmr7PS/o34MvAmZIakn59EN9PRETUoOo01Am2V7U22u6WdELVzm0vAZa0tF3dtHwrcOs+PntG1f4jImJ4VI0sxg+w7bChLCQiIg5cVWGxXNLFrY2SLgLuraekiIg40FSFxR8AHyhfxfHZ8utbwP8Efr/+8iIi2mfTpk1cdtllPPbYY+0upe0GDAvbP7H9RuBPgQ3l15/afoPtH9dfXkRE+yxYsIBVq1axYMGCdpfSdlW3zh4u6VDbd9q+HrgdeI2kc4envIiI9ti0aRO33XYbtrntttsO+tFF1d1Q3wQuAtZJmgLcA/wD8DZJ02xfVXeBdahr+tO6rFu3DqhvcqW6ZLrWGMkWLFiAXbx0Yvfu3SxYsIAPf/jDba6qfarC4ijb68rlucA/2b6sfMjuXooH6Uacnp4eVty/ht2Hv6TdpQyKthf/w977w5Fz5m/MU5vbXULE83LHHXewY8cOAHbs2MHtt9+esBhA87uc3kzx2nBsb5e0u7aqhsHuw1/C01Pf1u4yRq3xazINSYxsZ511FkuWLGHHjh0ceuihnH322e0uqa2q7oZaJek6SR8CplBcs0DSi2uvLCKijebOnYtUvA91zJgxzJ07t80VtVdVWFwMbAJOAM62/VTZPpVi4qKIiFFp0qRJzJo1C0nMmjWLiRMHPXvCqDTgaSjb24BPAUjqkNRhu9f23cDdw1FgRES7zJ07lw0bNhz0owqovnVWkv6XpF7gP4EHJfVKunqgz0VEjAaTJk3i+uuvP+hHFTC4J7h/FZhme6Lto4DTgNPL6xgREXEQqAqL9wNzbP9XX4Pt9cD7ym0REXEQqAqLQ21vam203QscWk9JERFxoKkKi+37uS0iIkaRqrB4jaT/7ufrSeDVVTuXNFPSWkk9kq7sZ/vxkpZJWlW+2bazadtcSevKr9yKEBHRRlW3zo7d3x1LGgvcAJwFNCjmxlhse01Tt+uAL9peIOnNwLXABZJeAvwvoIviKfJ7y88+vr/1RETE/qsaWTwf04Ae2+ttbwcWArNb+kwFlpXLdzZt/3XgDtuby4C4A5hZY60RETGAOsNiMvBI03qjbGu2EjivXD4XOFLSxEF+FkmXSOqW1N3b2ztkhUdExLPVGRbqp80t61cA0yWtAKYDjwI7B/lZbN9ou8t2V0dHx/OtNyIi9qHqrbPPRwM4tmm9E9jY3MH2RuAdAJImAOfZ3iKpAcxo+exdNdYaEREDqHNksRw4SdKJ5fwX5wOLmztImiSpr4argJvK5aXA2ZKOknQUcHbZFhERbVBbWNjeCVxK8Uv+AWCR7dWS5kk6p+w2A1gr6UHgaOCa8rObgT+jCJzlwLyyLSIi2qDO01DYXgIsaWm7umn5VuDWfXz2JvaMNCIioo3qPA0VERGjRMIiIiIqJSwiIqJSwiIiIiolLCIiolLCIiIiKiUsIiKiUsIiIiIqJSwiIqJSwiIiIiolLCIiolLCIiIiKiUsIiKiUsIiIiIqJSwiIqJSrWEhaaaktZJ6JF3Zz/bjJN0paYWkVZLeWraPk/QFSfdLWilpRp11RkTEwGoLC0ljgRuAWcBUYI6kqS3dPkYxg95rKaZd/euy/WIA268GzgI+2zT9akREDLM6fwFPA3psr7e9HVgIzG7pY+CF5fKLgI3l8lRgGYDtnwJPAF011hoREQOoMywmA480rTfKtmafAN4nqUEx/eplZftKYLakQySdCLwOOLb1AJIukdQtqbu3t3eo64+IiFKdYaF+2tyyPge42XYn8FbglvJ0000U4dINfA64G9i5187sG2132e7q6OgY0uIjImKPQ2rcd4NnjwY62XOaqc9FwEwA2/dIGg9MKk89faivk6S7gXU11hoREQOoc2SxHDhJ0omSxlFcwF7c0udh4EwASScD44FeSYdLOqJsPwvYaXtNjbVGRMQAahtZ2N4p6VJgKTAWuMn2aknzgG7bi4GPAH8r6UMUp6gutG1JvwAslbQbeBS4YChrazQajHlqC+PXfH0odxtNxjz1GI3GXmcOI2KEqvM0FLaXUFy4bm67uml5DXB6P5/bAPxSnbVFRMTg1RoWB6rOzk5+8swhPD31be0uZdQav+brdHa+tN1lRMQQyYNuERFRKWERERGVEhYREVEpYREREZUSFhERUSlhERERlRIWERFRKWERERGVEhYREVEpYREREZUSFhERUSlhERERlRIWERFRKWERERGVag0LSTMlrZXUI+nKfrYfJ+lOSSskrZL01rL9UEkLJN0v6QFJV9VZZ0REDKy2sJA0FrgBmAVMBeZImtrS7WPAItuvpZh29a/L9ncCL7D9auB1wG9LOqGuWiMiYmB1jiymAT2219veDiwEZrf0MfDCcvlFwMam9iMkHQIcBmwH/rvGWiMiYgB1hsVk4JGm9UbZ1uwTwPskNSimX72sbL8V+BnwI+Bh4Drbm1sPIOkSSd2Sunt7e4e4/IiI6FNnWKifNreszwFutt0JvBW4RdIYilHJLuAY4ETgI5JettfO7Bttd9nu6ujoGNrqIyLi5+oMiwZwbNN6J3tOM/W5CFgEYPseYDwwCXgP8E3bO2z/FPgO0FVjrRERMYA6w2I5cJKkEyWNo7iAvbilz8PAmQCSTqYIi96y/c0qHAH8CvCfNdYaEREDqC0sbO8ELgWWAg9Q3PW0WtI8SeeU3T4CXCxpJfBPwIW2TXEX1QTgBxSh8wXbq+qqNSIiBnZInTu3vYTiwnVz29VNy2uA0/v53FaK22cjIuIAkCe4IyKiUsIiIiIqJSwiIqJSrdcsDmRjntrM+DVfb3cZg6Kni4fXPf6FFT0PHGOe2gy8tN1lRMQQOSjDYsqUKe0u4TlZt+5JAE56+Uj65fvSEffvHBH7dlCGxeWXX97uEp6Tvnrnz5/f5koi4mCVaxYREVEpYREREZUSFhERUSlhERERlRIWERFRKWERERGVEhYREVEpYREREZUSFhERUanWsJA0U9JaST2Sruxn+3GS7pS0QtIqSW8t298r6b6mr92STq2z1oiI2LfawkLSWIoZ72YBU4E5kqa2dPsYxQx6r6WYdvWvAWz/g+1TbZ8KXABssH1fXbVGRMTA6hxZTAN6bK+3vR1YCMxu6WOg71WqLwI29rOfORRTrkZERJvU+SLBycAjTesN4LSWPp8Abpd0GXAE8JZ+9vNu9g6ZiIgYRnWOLNRPm1vW5wA32+4E3grcIunnNUk6DXjK9g/6PYB0iaRuSd29vb1DVXdERLSoMywawLFN653sfZrpImARgO17gPHApKbt5zPAKSjbN9rust3V0dExJEVHRMTe6gyL5cBJkk6UNI7iF//ilj4PA2cCSDqZIix6y/UxwDsprnVEREQb1RYWtncClwJLgQco7npaLWmepHPKbh8BLpa0kmIEcaHtvlNVbwIattfXVWNERAxOrTPl2V4CLGlpu7ppeQ1w+j4+exfwK3XWFxERg5MnuCMiolLCIiIiKiUsIiKiUsIiIiIqJSwiIqJSwiIiIirVeuvswWb+/Pn09PQM+X7XrVsHwOWXXz7k+waYMmVKbfuOiNEhYTECHHbYYe0uISIOcgmLIZS/ziNitMo1i4iIqJSwiIiISgmLiIiolLCIiIhKCYuIiKiUsIiIiEoJi4iIqJSwiIiIStozi+nIJqkXeKjdddRoErCp3UXEfsvPb+Qa7T+74213VHUaNWEx2knqtt3V7jpi/+TnN3LlZ1fIaaiIiKiUsIiIiEoJi5HjxnYXEM9Lfn4jV3525JpFREQMQkYWERFRKWERERGVEhYHCEkvlbRQ0g8lrZG0RNIrJM2X9ANJ90taLunEsv+Gsu2+8uuN7f4eDnaSjpb0j5LWS7pX0j2SzpU0Q5IlXdTU97Vl2xXtrDlA0l9K+oOm9aWSPt+0/llJHy5/Xn/W1D5J0g5J/3u4a26HhMUBQJKArwJ32X657anAR4F3A8cAp9h+NXAu8ETTR3/N9qnl193DXnj8XPkz/Brwbdsvs/064Hygs+xyP8XPs8/5wMrhrTL24W7gjQCSxlA8hPfKpu1vBL4DrAfe1tT+TmD1MNXYdgmLA8OvATts/01fg+37gJ8BP7K9u2xr2H68TTXGwN4MbG/5GT5k+/py9WFgfDn6EDATuK0NdcbevkMZFhQh8QPgSUlHSXoBcDLwOLANeEBS3wN67wYWDXex7ZI5uA8MrwLu7ad9EfDvks4AlgF/b3tF0/Y7Je0CnrF92jDUGfv2SuD7FX1upfhrdEXZ95m6i4pqtjdK2inpOIrQuAeYDLwB2AKsAraX3RcC50v6MbAL2Egx+h/1MrI4gNluAL8EXAXsBpZJOrOpS99pqATFAUbSDZJWSlre1LyIIizmAP/UnspiH/pGF31hcU/TevMp3m8CZ1H8DL80zDW2VcLiwLAaeF1/G2w/Y/s2238I/Dnw9mGtLAZrNfDLfSu2PwicCXQ0tf0Y2EHxy2bZcBcYA+q7bvFqitNQ36UYWfRdrwDA9naKswAfAb4y/GW2T8LiwPCvwAskXdzXIOn1kqZLOqZcHwOcwuh+s+5I9q8U1yR+t6nt8H76XQ38se1dw1NWDNJ3KC5eb7a9y/Zm4MUUgXFPS9/PUvwMHxvmGtsq1ywOALYt6Vzgc5KuBJ4GNlAMef+ivMgG8D3goLhNb6Qpf4ZvB/5S0h8BvRQ3KPxxS7/ctXZgup/iLqh/bGmbYHuTpAl9jbZXcxDdBdUnr/uIiIhKOQ0VERGVEhYREVEpYREREZUSFhERUSlhERERlXLrbBxUJE1kzwNxL6V4ZUNvuf6U7WF5e29FHdPKh78iDhi5dTYOWpI+AWy1fV3qiBhYTkNFlCRtLf/7i5K+Xc4T8gNJZ0gaK+nmprlFPlT2vavvLaTl/AYbyuWxkj5TzkGyStJvP4c6rpX0wab1T0v6PUlvkXSnpK+Vc57cUL7BFkmzyvkzvi/pS5KOGMJ/moiERUQ/3gMstX0q8BrgPuBUYLLtV5Vzi3yhYh8XAVtsvx54PXBx38RVg/B54EIoQofi5YN9Lx48DfgDincYnQzMlvQLwJXAmbZ/meItqb8/yGNFDEquWUTsbTlwk6RDga/Zvk/SeuBlkq4HvgHcXrGPs4FTJP1muf4i4CTgv6oObvuHkp6U9GrgeOB7th8vBxHftb0BQNJC4FfLj00F7i77jAP+fdDfbcQgJCwiWtj+tqQ3Af8DuEXSZ2x/UdJrgF8HPgi8C/gtYCd7Rujjm3Yj4DLbS/ezjL+jGF2cAPzf5vJayy2P9U3bF+znsSIq5TRURAtJxwM/tf23FL+0f1nSJGCM7a8AH2fP68g3sOf18r/ZtJulwO+WoxPK+dSfy3WErwC/QXH661+a2n9F0nHl6al3UYwg7gamS3pZeawjJJ30HI4VUSkji4i9zQD+UNIOYCvwfoqZ075QvioeigmpAK4DFkm6gOI15X0+TzEq+H55EbqX5zAXie2nJX0b+HHftLqluylekf1K4C5gcfnG24uAL0kaV/b7KLBusMeLqJJbZyMOQGUo3Qe83fb6su0twKW2MwFWDLuchoo4wJQXtn9IcR1ifbvriYCMLCIiYhAysoiIiEoJi4iIqJSwiIiISgmLiIiolLCIiIhK/x8Jb341fsd9XgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.88937527, 0.93763281, 0.92988741])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GT_data_path =\"H:/MISA_results/val_gt_pre/\"    \n",
    "Segmented_data_path =\"H:/MISA_results/nnet_finalData_results/FineTuned_2000steps_50Samples/val/\"\n",
    "\n",
    "visualize_dices(Segmented_data_path,GT_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Pretrained with 50 samples 2000step+2000step...model Here is the model Name in the folder 1546123949, saved in the destination folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the Dice for Validation Image:  IBSR_11_seg.nii.gz\n",
      "CSF DICE = 0.887657561323353 GM DICE = 0.9437249759410775 WM DICE = 0.951566447263359\n",
      "Print the Dice for Validation Image:  IBSR_12_seg.nii.gz\n",
      "CSF DICE = 0.8894814532641303 GM DICE = 0.930580265784616 WM DICE = 0.9346678880727448\n",
      "Print the Dice for Validation Image:  IBSR_13_seg.nii.gz\n",
      "CSF DICE = 0.881584613176403 GM DICE = 0.9340039539793288 WM DICE = 0.906592900419352\n",
      "Print the Dice for Validation Image:  IBSR_14_seg.nii.gz\n",
      "CSF DICE = 0.9130157718655408 GM DICE = 0.9500263177199377 WM DICE = 0.9411605106271999\n",
      "Print the Dice for Validation Image:  IBSR_17_seg.nii.gz\n",
      "CSF DICE = 0.9212611638356981 GM DICE = 0.9481097297434724 WM DICE = 0.9336152211160325\n",
      "###Printing Avg ± Standard Daviation###\n",
      "Avg CSF DICE = 0.8986001126930251 ± 0.017419465206538552\n",
      "Avg GM DICE = 0.9412890486336865 ± 0.008610254612456939\n",
      "Avg WM DICE = 0.9335205934997377 ± 0.016661685581199185\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.89860011, 0.94128905, 0.93352059])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GT_data_path =\"H:/MISA_results/val_gt_pre/\"    \n",
    "Segmented_data_path =\"H:/MISA_results/nnet_finalData_results/FineTuned_2000_and2000steps_50Samples/val/\"\n",
    "\n",
    "visualize_dices(Segmented_data_path,GT_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## samples 50 with additional training ****Best Model >>Folder  Folder name 1546148883"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the Dice for Validation Image:  IBSR_11_seg.nii.gz\n",
      "CSF DICE = 0.8780283774653325 GM DICE = 0.9428314749509243 WM DICE = 0.9452840736535251\n",
      "Print the Dice for Validation Image:  IBSR_12_seg.nii.gz\n",
      "CSF DICE = 0.8887151049812787 GM DICE = 0.9350217644644564 WM DICE = 0.9387972048850052\n",
      "Print the Dice for Validation Image:  IBSR_13_seg.nii.gz\n",
      "CSF DICE = 0.8782430908065426 GM DICE = 0.9334339704019193 WM DICE = 0.9069717262282514\n",
      "Print the Dice for Validation Image:  IBSR_14_seg.nii.gz\n",
      "CSF DICE = 0.9078964725747303 GM DICE = 0.9497677791249732 WM DICE = 0.9386477656476515\n",
      "Print the Dice for Validation Image:  IBSR_17_seg.nii.gz\n",
      "CSF DICE = 0.9220750388514763 GM DICE = 0.9470593410774371 WM DICE = 0.9355325817044791\n",
      "###Printing Avg ± Standard Daviation###\n",
      "Avg CSF DICE = 0.894991616935872 ± 0.019414596730773614\n",
      "Avg GM DICE = 0.9416228660039421 ± 0.00721089729638467\n",
      "Avg WM DICE = 0.9330466704237825 ± 0.015002311670201258\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.89499162, 0.94162287, 0.93304667])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GT_data_path =\"H:/MISA_results/val_gt_pre/\"    \n",
    "Segmented_data_path =\"H:/MISA_results/nnet_finalData_results/FineTuned_2000_and2000plus4000steps_50Samples//val/\"\n",
    "\n",
    "visualize_dices(Segmented_data_path,GT_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Balanc Sampling\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## Using Pretrained Model For Extraced_Balanced Sample.50 samples.4000 steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the Dice for Validation Image:  IBSR_11_seg.nii.gz\n",
      "CSF DICE = 0.8771414898707928 GM DICE = 0.9385358655547226 WM DICE = 0.9497688157868626\n",
      "Print the Dice for Validation Image:  IBSR_12_seg.nii.gz\n",
      "CSF DICE = 0.8855806630857567 GM DICE = 0.9244100815384995 WM DICE = 0.9297861375934038\n",
      "Print the Dice for Validation Image:  IBSR_13_seg.nii.gz\n",
      "CSF DICE = 0.8762862251610944 GM DICE = 0.9223402221807125 WM DICE = 0.8864924481480346\n",
      "Print the Dice for Validation Image:  IBSR_14_seg.nii.gz\n",
      "CSF DICE = 0.8949802361329992 GM DICE = 0.949175148181537 WM DICE = 0.94437823776144\n",
      "Print the Dice for Validation Image:  IBSR_17_seg.nii.gz\n",
      "CSF DICE = 0.9243908848594061 GM DICE = 0.9396759038369784 WM DICE = 0.9225687697322523\n",
      "###Printing Avg ± Standard Daviation###\n",
      "Avg CSF DICE = 0.8916758998220098 ± 0.019789087612387634\n",
      "Avg GM DICE = 0.9348274442584901 ± 0.011264642891782988\n",
      "Avg WM DICE = 0.9265988818043986 ± 0.02493931515905085\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.8916759 , 0.93482744, 0.92659888])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GT_data_path =\"H:/MISA_results/val_gt_pre/\"    \n",
    "Segmented_data_path =\"H:/MISA_results/nnet_finalData_results/FineTuned_4000_Steps_50samples_balanced_samples/val/\"\n",
    "\n",
    "visualize_dices(Segmented_data_path,GT_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## training_fromScratech_4000step_classbalanced training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the Dice for Validation Image:  IBSR_11_seg.nii.gz\n",
      "CSF DICE = 0.8438862006245925 GM DICE = 0.9355752036253576 WM DICE = 0.9360343230461704\n",
      "Print the Dice for Validation Image:  IBSR_12_seg.nii.gz\n",
      "CSF DICE = 0.871569541951004 GM DICE = 0.9340009489151639 WM DICE = 0.9371753562401044\n",
      "Print the Dice for Validation Image:  IBSR_13_seg.nii.gz\n",
      "CSF DICE = 0.8706167757112402 GM DICE = 0.9367385823846297 WM DICE = 0.9166770281041449\n",
      "Print the Dice for Validation Image:  IBSR_14_seg.nii.gz\n",
      "CSF DICE = 0.9026184988754061 GM DICE = 0.9453581065519315 WM DICE = 0.9302700748199038\n",
      "Print the Dice for Validation Image:  IBSR_17_seg.nii.gz\n",
      "CSF DICE = 0.9097983592731733 GM DICE = 0.9360045013853625 WM DICE = 0.9074928944935405\n",
      "###Printing Avg ± Standard Daviation###\n",
      "Avg CSF DICE = 0.8796978752870832 ± 0.026750730533647685\n",
      "Avg GM DICE = 0.937535468572489 ± 0.004486313100777879\n",
      "Avg WM DICE = 0.9255299353407729 ± 0.012962790212128114\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.87969788, 0.93753547, 0.92552994])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GT_data_path =\"H:/MISA_results/val_gt_pre/\"    \n",
    "Segmented_data_path =\"H:/MISA_results/nnet_finalData_results/training_fromScratech_4000step_classbalancedtraining/val/\"\n",
    "\n",
    "visualize_dices(Segmented_data_path,GT_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## training_fromScratech_4000step_class random 50 samplestraining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the Dice for Validation Image:  IBSR_11_seg.nii.gz\n",
      "CSF DICE = 0.8759024147373662 GM DICE = 0.9235291419300478 WM DICE = 0.9407176753998484\n",
      "Print the Dice for Validation Image:  IBSR_12_seg.nii.gz\n",
      "CSF DICE = 0.8745024374972047 GM DICE = 0.9179978836444639 WM DICE = 0.9329688671162409\n",
      "Print the Dice for Validation Image:  IBSR_13_seg.nii.gz\n",
      "CSF DICE = 0.8693974575395321 GM DICE = 0.9347193116340363 WM DICE = 0.915457483045702\n",
      "Print the Dice for Validation Image:  IBSR_14_seg.nii.gz\n",
      "CSF DICE = 0.8975435323383084 GM DICE = 0.9374110945594462 WM DICE = 0.9295850448522546\n",
      "Print the Dice for Validation Image:  IBSR_17_seg.nii.gz\n",
      "CSF DICE = 0.9164754895663185 GM DICE = 0.941242601502198 WM DICE = 0.9190548479203374\n",
      "###Printing Avg ± Standard Daviation###\n",
      "Avg CSF DICE = 0.886764266335746 ± 0.019804593379984296\n",
      "Avg GM DICE = 0.9309800066540384 ± 0.009807051664095108\n",
      "Avg WM DICE = 0.9275567836668767 ± 0.01031127476195157\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.88676427, 0.93098001, 0.92755678])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GT_data_path =\"H:/MISA_results/val_gt_pre/\"    \n",
    "Segmented_data_path =\"H:/MISA_results/nnet_finalData_results/tu/\"\n",
    "\n",
    "visualize_dices(Segmented_data_path,GT_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# different Patch Size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## 32 patch size 200 samples 4000 steps\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the Dice for Validation Image:  IBSR_11_seg.nii.gz\n",
      "CSF DICE = 0.09691578392930297 GM DICE = 0.7596137902144284 WM DICE = 0.8033337387852763\n",
      "Print the Dice for Validation Image:  IBSR_12_seg.nii.gz\n",
      "CSF DICE = 0.07588323104992536 GM DICE = 0.6683708676417472 WM DICE = 0.7020870882203888\n",
      "Print the Dice for Validation Image:  IBSR_13_seg.nii.gz\n",
      "CSF DICE = 0.09310548025928109 GM DICE = 0.6563687201370636 WM DICE = 0.629080822990513\n",
      "Print the Dice for Validation Image:  IBSR_14_seg.nii.gz\n",
      "CSF DICE = 0.04724238812439051 GM DICE = 0.7306315428693557 WM DICE = 0.7383388329073317\n",
      "Print the Dice for Validation Image:  IBSR_17_seg.nii.gz\n",
      "CSF DICE = 0.03275248266816563 GM DICE = 0.7246907639263218 WM DICE = 0.7106345161197845\n",
      "###Printing Avg ± Standard Daviation###\n",
      "Avg CSF DICE = 0.06917987320621313 ± 0.028261539483919244\n",
      "Avg GM DICE = 0.7079351369577834 ± 0.04384913861489262\n",
      "Avg WM DICE = 0.7166949998046588 ± 0.06304976432489022\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.06917987, 0.70793514, 0.716695  ])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GT_data_path =\"H:/MISA_results/val_gt_pre/\"    \n",
    "Segmented_data_path =\"H:/MISA_results/nnet_finalData_results/patch32/val/\"\n",
    "\n",
    "visualize_dices(Segmented_data_path,GT_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## 64 patch size 200 samples 4000 steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the Dice for Validation Image:  IBSR_11_seg.nii.gz\n",
      "CSF DICE = 0.78179931640625 GM DICE = 0.9109983401122642 WM DICE = 0.9165718913865828\n",
      "Print the Dice for Validation Image:  IBSR_12_seg.nii.gz\n",
      "CSF DICE = 0.7616160255114645 GM DICE = 0.8870182153924365 WM DICE = 0.8843134104598543\n",
      "Print the Dice for Validation Image:  IBSR_13_seg.nii.gz\n",
      "CSF DICE = 0.7668081521563037 GM DICE = 0.8805804519281399 WM DICE = 0.8316690458849235\n",
      "Print the Dice for Validation Image:  IBSR_14_seg.nii.gz\n",
      "CSF DICE = 0.8272017837235228 GM DICE = 0.9224309704428758 WM DICE = 0.9054039670383252\n",
      "Print the Dice for Validation Image:  IBSR_17_seg.nii.gz\n",
      "CSF DICE = 0.8705533406521446 GM DICE = 0.916153733703158 WM DICE = 0.8939442949183506\n",
      "###Printing Avg ± Standard Daviation###\n",
      "Avg CSF DICE = 0.8015957236899371 ± 0.046398470849105505\n",
      "Avg GM DICE = 0.903436342315775 ± 0.0185179532423489\n",
      "Avg WM DICE = 0.8863805219376074 ± 0.032894678430308556\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.80159572, 0.90343634, 0.88638052])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GT_data_path =\"H:/MISA_results/val_gt_pre/\"    \n",
    "Segmented_data_path =\"G:/64/\"\n",
    "\n",
    "visualize_dices(Segmented_data_path,GT_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 128 patch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the Dice for Validation Image:  IBSR_11_seg.nii.gz\n",
      "CSF DICE = 0.8759024147373662 GM DICE = 0.9235291419300478 WM DICE = 0.9407176753998484\n",
      "Print the Dice for Validation Image:  IBSR_12_seg.nii.gz\n",
      "CSF DICE = 0.8745024374972047 GM DICE = 0.9179978836444639 WM DICE = 0.9329688671162409\n",
      "Print the Dice for Validation Image:  IBSR_13_seg.nii.gz\n",
      "CSF DICE = 0.8693974575395321 GM DICE = 0.9347193116340363 WM DICE = 0.915457483045702\n",
      "Print the Dice for Validation Image:  IBSR_14_seg.nii.gz\n",
      "CSF DICE = 0.8975435323383084 GM DICE = 0.9374110945594462 WM DICE = 0.9295850448522546\n",
      "Print the Dice for Validation Image:  IBSR_17_seg.nii.gz\n",
      "CSF DICE = 0.9164754895663185 GM DICE = 0.941242601502198 WM DICE = 0.9190548479203374\n",
      "###Printing Avg ± Standard Daviation###\n",
      "Avg CSF DICE = 0.886764266335746 ± 0.019804593379984296\n",
      "Avg GM DICE = 0.9309800066540384 ± 0.009807051664095108\n",
      "Avg WM DICE = 0.9275567836668767 ± 0.01031127476195157\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.88676427, 0.93098001, 0.92755678])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GT_data_path =\"H:/MISA_results/val_gt_pre/\"    \n",
    "Segmented_data_path =\"H:/MISA_results/nnet_finalData_results/tu/\"\n",
    "\n",
    "visualize_dices(Segmented_data_path,GT_data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.5.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {
    "height": "calc(100% - 180px)",
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   },
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   "toc_window_display": true
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 },
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}