MISA_PROJECT_BASIC_UNET_REPOSITORY_.ipynb

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    "colab": {
      "name": "MISA PROJECT BASIC UNET  REPOSITORY .ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dp5qwtTLMuwR",
        "colab_type": "text"
      },
      "source": [
        "# MISA PROJECT: BRAIN TISSUES SEGMENTATION\n",
        "Segmenting GM WM and CSF of BRAIN MRI images\n",
        "\n",
        "**Esteban Vaca, Sergio Tascon, Jhon Gomez**\n",
        "\n",
        "*Universitat de Girona, Spain*\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QEdsLwVAM1VN",
        "colab_type": "text"
      },
      "source": [
        "# 1.IMPORT DEPENDENCIES\n",
        "---\n",
        "\n",
        "First of all, import or install all necessary libraries and google drive. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G6YVuVQVMmww",
        "colab_type": "code",
        "outputId": "9b0e4998-2f16-4c3c-9f0f-a3d7c36565ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "source": [
        "!pip install https://github.com/ANTsX/ANTsPy/releases/download/v0.1.8/antspy-0.1.7-cp36-cp36m-linux_x86_64.whl\n",
        "!pip install statsmodels\n",
        "!pip install SimpleITK  "
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting antspy==0.1.7\n",
            "\u001b[?25l  Downloading https://github.com/ANTsX/ANTsPy/releases/download/v0.1.8/antspy-0.1.7-cp36-cp36m-linux_x86_64.whl (255.7MB)\n",
            "\u001b[K     |████████████████████████████████| 255.7MB 55kB/s \n",
            "\u001b[?25hInstalling collected packages: antspy\n",
            "Successfully installed antspy-0.1.7\n",
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.6/dist-packages (0.10.2)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.6/dist-packages (from statsmodels) (1.17.5)\n",
            "Requirement already satisfied: patsy>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from statsmodels) (0.5.1)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.6/dist-packages (from statsmodels) (0.25.3)\n",
            "Requirement already satisfied: scipy>=0.18 in /usr/local/lib/python3.6/dist-packages (from statsmodels) (1.4.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from patsy>=0.4.0->statsmodels) (1.12.0)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19->statsmodels) (2.6.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19->statsmodels) (2018.9)\n",
            "Collecting SimpleITK\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f8/d8/53338c34f71020725ffb3557846c80af96c29c03bc883551a2565aa68a7c/SimpleITK-1.2.4-cp36-cp36m-manylinux1_x86_64.whl (42.5MB)\n",
            "\u001b[K     |████████████████████████████████| 42.5MB 72kB/s \n",
            "\u001b[?25hInstalling collected packages: SimpleITK\n",
            "Successfully installed SimpleITK-1.2.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bB9bIKv6NMj6",
        "colab_type": "text"
      },
      "source": [
        "## Mount Google drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BcEFIiYCNIal",
        "colab_type": "code",
        "outputId": "67bd1457-c0f9-4efe-f853-c72f7f478451",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive', force_remount=True)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FmNGVsFiO-yy",
        "colab_type": "text"
      },
      "source": [
        "Import the required libraries:\n",
        "  \n",
        "  Inide the imported libraries is possible to find the following packages:\n",
        "\n",
        "\n",
        "*   Unet basic model\n",
        "*   Training Functions\n",
        "*   File managers\n",
        "*   Early Stopping functions\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FFG5t1a_NlaC",
        "colab_type": "code",
        "outputId": "1eb0cb07-edf3-4cf8-ab32-b556194275aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133
        }
      },
      "source": [
        "import sys\n",
        "sys.path.append('/content/gdrive/My Drive/UNET')\n",
        "from actions.find_ratio import find_ratio\n",
        "from actions.early_stopping import EarlyStopping\n",
        "from models.unet_instance_norm import UnetModel  \n",
        "from utils.utils import *\n",
        "from file_manager.file_manager import *\n",
        "from actions.train_model import *\n",
        "from actions.evaluate_subject import *\n",
        "from metrics.metrics import calculate_dices\n",
        "from models.models import Unet\n",
        "from preprocessing.preprocessing import histogram_matching, normalize_images, bias_field_correction\n",
        "import ants\n",
        "import pandas as pd\n",
        "from importlib import reload  # Py3 only; unneeded in py2."
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", FutureWarning)\n",
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "metadata": {
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z_AxfFA3PZr3",
        "colab_type": "text"
      },
      "source": [
        "## 1.1.Data Loading\n",
        "---\n",
        "\n",
        "Let's now obtain the data to train and test the model. This data is available in google drive and will be copied to the colab directory to speed-up the training process. The data corresponds to the `IBSR18` dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DWGAK_dZOp_m",
        "colab_type": "code",
        "outputId": "baf1a545-fad7-4fcf-f518-9c4a7cf8164f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "!mkdir data\n",
        "!cp -r /content/gdrive/My\\ Drive/MISAPROJ_REPORT data/dataset/\n",
        "!ls data/dataset/Training_Set/IBSR_01/"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "IBSR_01.nii.gz\tIBSR_01_norm.nii.gz  IBSR_01_ROI.nii.gz  IBSR_01_seg.nii.gz\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jNifpNKVQI8f",
        "colab_type": "text"
      },
      "source": [
        "This is the set of the Dataset IBSR18 and it is splited in three sets, training, validation and testing. Each case contains a `T1` image and its tissue segmentation. Manual annotations include white matter `class=1`, grey  matter  `class=2` and CSF `class=3`. \n",
        "\n",
        "Let's see a case as example: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wd14jjgmQE3f",
        "colab_type": "code",
        "outputId": "d79c83f3-1ad1-403f-de0e-60495f91591b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        }
      },
      "source": [
        "mri_subject = ants.image_read('data/dataset/Training_Set/IBSR_01/IBSR_01.nii.gz')\n",
        "segmentation_mask = ants.image_read('data/dataset/Training_Set/IBSR_01/IBSR_01_seg.nii.gz')\n",
        "mri_subject.plot(overlay=segmentation_mask, nslices=9, axis=2, title='Subject 1: Brain Tissue Segmentation', overlay_cmap = 'brg' )"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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pvpsp66u8W/wIOEREvoPWEgwBP1VK+YL1MMwgcJm3yQqLiyVLxPyFCgf94zF5LNEpZzaG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\nRcH/B+2iu26yZV2zAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x432 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7j0zGWiemgMf",
        "colab_type": "text"
      },
      "source": [
        "# 2.Preprocess the data\n",
        "---\n",
        "\n",
        "We will perform an histogram matching to ensure that the intensities of all the images match.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "85a1Xk4ErfbI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import SimpleITK as sitk\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fixed_path = '/content/data/dataset/Training_Set/IBSR_01/IBSR_01.nii.gz'\n",
        "moving_path = '/content/data/dataset/Training_Set/IBSR_03/IBSR_03.nii.gz'\n",
        "fixed_itk = sitk.ReadImage(fixed_path)\n",
        "moving_itk = sitk.ReadImage(moving_path)\n",
        "moving_matched_itk = histogram_matching(fixed_itk, moving_itk)\n",
        "sitk.WriteImage(moving_matched_itk, '/content/data/dataset/Training_Set/IBSR_03/IBSR_03_norm.nii.gz')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9fR_bCMxsdZb",
        "colab_type": "code",
        "outputId": "f98bcb77-a3fc-4aef-c66d-dca18399dbd4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "fixed_img = sitk.GetArrayFromImage(fixed_itk)\n",
        "moving_img = sitk.GetArrayFromImage(moving_itk)\n",
        "moving_matched_img = sitk.GetArrayFromImage(moving_matched_itk)\n",
        "\n",
        "fig, ax = plt.subplots(1,2,figsize = (12, 6))\n",
        "out_vals = ax[0].hist(fixed_img[fixed_img>0], 100, label = 'Fixed MRI', alpha = 0.5, log = True)\n",
        "ax[0].hist(moving_img.ravel(), out_vals[1], label = 'Moving MRI', alpha = 0.5, log = True)\n",
        "ax[0].legend()\n",
        "\n",
        "out_vals = ax[1].hist(fixed_img[fixed_img>0], 100, label = 'Fixed MRI', alpha = 0.5, log = True)\n",
        "ax[1].hist(moving_matched_img.ravel(), out_vals[1], label = 'Normalized MRI', alpha = 0.5, log = True)\n",
        "ax[1].legend()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fe9d2fa1518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WrMjos771rW/ppptu0uDBg/WFL3xBQ4YMUWun881MCxYsaHO7U6ZM0eLFi5MX9CHc7r77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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wcfs2lTdoPQo",
        "colab_type": "text"
      },
      "source": [
        "Additionaly we will get the ratio of the labels between CSF, GM, WM and background:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LgCkcPYgVDDY",
        "colab_type": "text"
      },
      "source": [
        "# 3.Data Preparation\n",
        "---\n",
        "Once we have our dataset in the colab memmory, we will proceed to build the training, validation and test structures for Pytorch which includes`Dataset` and `Dataloader` objects.\n",
        "\n",
        "\n",
        "With the aim  to unify all the hyper-parameters and training options, we will define a dictionary `options` that we will populate with new values.\n",
        "\n",
        "First we will include in options the folders where the dataset is located."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tLWHrI_bQQHA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "options = {}\n",
        "# training data path\n",
        "options['training_path'] = '/content/data/dataset/Training_Set'\n",
        "\n",
        "# Validation data path \n",
        "options['val_path'] = '/content/data/dataset/Validation_Set'\n",
        "\n",
        "# Validation data path \n",
        "options['test_path'] = '/content/data/dataset/Test_Set'\n",
        "\n",
        "# train/validation split percentage\n",
        "options['val_split']  = 0.5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ubq5Yg0HsIdV",
        "colab_type": "text"
      },
      "source": [
        "Now we will create the region of interest of the MRI volumes. This will be stored in a folder called roi together with the dataset data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rzaREXfGV4Sd",
        "colab_type": "code",
        "outputId": "b46085a3-451e-4f06-9657-e9d27f5857ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        }
      },
      "source": [
        "create_ROIs(options['training_path'])\n",
        "create_ROIs(options['val_path'])\n",
        "create_ROIs(options['test_path'])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Creating ROI for:  IBSR_04\n",
            "Creating ROI for:  IBSR_09\n",
            "Creating ROI for:  IBSR_18\n",
            "Creating ROI for:  IBSR_05\n",
            "Creating ROI for:  IBSR_06\n",
            "Creating ROI for:  IBSR_01\n",
            "Creating ROI for:  IBSR_03\n",
            "Creating ROI for:  IBSR_08\n",
            "Creating ROI for:  IBSR_16\n",
            "Creating ROI for:  IBSR_07\n",
            "Creating ROI for:  IBSR_11\n",
            "Creating ROI for:  IBSR_13\n",
            "Creating ROI for:  IBSR_17\n",
            "Creating ROI for:  IBSR_14\n",
            "Creating ROI for:  IBSR_12\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zYUK4v8w2N3k",
        "colab_type": "text"
      },
      "source": [
        "Now we will Normalize all the images in the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p4spLF-y2K-v",
        "colab_type": "code",
        "outputId": "d4d9cfc3-b7e3-40bf-bcd4-ba7265319850",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        }
      },
      "source": [
        "normalize_images(options['training_path'], fixed_path, '.nii.gz')\n",
        "normalize_images(options['val_path'], fixed_path, '.nii.gz')\n",
        "normalize_images(options['test_path'], fixed_path, '.nii.gz')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Normalizing image :  IBSR_04\n",
            "Normalizing image :  IBSR_09\n",
            "Normalizing image :  IBSR_18\n",
            "Normalizing image :  IBSR_05\n",
            "Normalizing image :  IBSR_06\n",
            "Normalizing image :  IBSR_01\n",
            "Normalizing image :  IBSR_03\n",
            "Normalizing image :  IBSR_08\n",
            "Normalizing image :  IBSR_16\n",
            "Normalizing image :  IBSR_07\n",
            "Normalizing image :  IBSR_11\n",
            "Normalizing image :  IBSR_13\n",
            "Normalizing image :  IBSR_17\n",
            "Normalizing image :  IBSR_14\n",
            "Normalizing image :  IBSR_12\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-XiO6BerzaB1",
        "colab_type": "text"
      },
      "source": [
        "Now, we can define the input dictionaries for training and validation: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I7wUIaxZyPIv",
        "colab_type": "code",
        "outputId": "61a4ba4a-d2fd-4be0-d0c6-66ef9e8ea435",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        }
      },
      "source": [
        "input_dictionary = create_training_validation_sets(options)\n",
        "print('Training Set:')\n",
        "input_dictionary['input_train_data']"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Set:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'IBSR_01': ['/content/data/dataset/Training_Set/IBSR_01/IBSR_01_norm.nii.gz'],\n",
              " 'IBSR_03': ['/content/data/dataset/Training_Set/IBSR_03/IBSR_03_norm.nii.gz'],\n",
              " 'IBSR_04': ['/content/data/dataset/Training_Set/IBSR_04/IBSR_04_norm.nii.gz'],\n",
              " 'IBSR_05': ['/content/data/dataset/Training_Set/IBSR_05/IBSR_05_norm.nii.gz'],\n",
              " 'IBSR_06': ['/content/data/dataset/Training_Set/IBSR_06/IBSR_06_norm.nii.gz'],\n",
              " 'IBSR_08': ['/content/data/dataset/Training_Set/IBSR_08/IBSR_08_norm.nii.gz'],\n",
              " 'IBSR_09': ['/content/data/dataset/Training_Set/IBSR_09/IBSR_09_norm.nii.gz'],\n",
              " 'IBSR_18': ['/content/data/dataset/Training_Set/IBSR_18/IBSR_18_norm.nii.gz']}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VgQsUzrIzfUL",
        "colab_type": "code",
        "outputId": "8cc85e21-ec0b-4c6f-d0a5-d2d363a598fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "print('Validation Set:')\n",
        "input_dictionary['input_val_data']"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Validation Set:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'IBSR_07': ['/content/data/dataset/Validation_Set/IBSR_07/IBSR_07_norm.nii.gz'],\n",
              " 'IBSR_16': ['/content/data/dataset/Validation_Set/IBSR_16/IBSR_16_norm.nii.gz']}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPfl8QnY0KQO",
        "colab_type": "text"
      },
      "source": [
        "Now we will set up other hiperparameters of the network, such as:\n",
        "\n",
        "* Patch size of `32x32x32`\n",
        "* Sampling step of `16x16x16`\n",
        "* Batch size of `32`\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qx5MMDHx0mhw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# additional options for patch size, sampling step, normalization\n",
        "options['patch_size'] = (32, 32, 32)\n",
        "options['sampling_step'] = (16, 16, 16)\n",
        "options['normalize'] = True \n",
        "options['batch_size'] = 64"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "73wIrCGI0gWh",
        "colab_type": "text"
      },
      "source": [
        "Now we will build the `Datasets` for training and validation data. Here, we need to define the patch size (in 3D), and how we are going to sample the images. Also consider image normalization (take it into account on testing also!). We normalize images with `mean=0` and `std=1`. In particular, we are going to use:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1r3UlmKFz-Ot",
        "colab_type": "code",
        "outputId": "46d39061-e40e-407e-8228-0c173cde28a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        }
      },
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "print('Training data: ')\n",
        "training_dataset = MRI_DataPatchLoader(input_data=input_dictionary['input_train_data'],\n",
        "                                       labels=input_dictionary['input_train_labels'],\n",
        "                                       rois=input_dictionary['input_train_rois'],\n",
        "                                       patch_size=options['patch_size'],\n",
        "                                       sampling_step=options['sampling_step'],\n",
        "                                       normalize=options['normalize'],\n",
        "                                       sampling_type = 'balanced+roi',\n",
        "                                       resample_csf = 5)\n",
        "\n",
        "training_dataloader = DataLoader(training_dataset, \n",
        "                                 batch_size=options['batch_size'],\n",
        "                                 shuffle=True)\n",
        "\n",
        "\n",
        "print('_________________________________________________')\n",
        "print('Validation data: ')\n",
        "validation_dataset = MRI_DataPatchLoader(input_data=input_dictionary['input_val_data'],\n",
        "                                        labels=input_dictionary['input_val_labels'],\n",
        "                                        rois=input_dictionary['input_val_rois'],\n",
        "                                        patch_size=options['patch_size'],\n",
        "                                        sampling_step=options['sampling_step'],\n",
        "                                        normalize=options['normalize'])\n",
        "\n",
        "validation_dataloader = DataLoader(validation_dataset, \n",
        "                                   batch_size=options['batch_size'],\n",
        "                                   shuffle=True)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training data: \n",
            "PADDED\n",
            "> DATA: Loaded scan IBSR_04 roi size: 1069342 label_size:  1027700\n",
            "PADDED\n",
            "> DATA: Loaded scan IBSR_09 roi size: 1002429 label_size:  927415\n",
            "PADDED\n",
            "> DATA: Loaded scan IBSR_18 roi size: 1668281 label_size:  1590195\n",
            "PADDED\n",
            "> DATA: Loaded scan IBSR_05 roi size: 1040178 label_size:  950111\n",
            "PADDED\n",
            "> DATA: Loaded scan IBSR_06 roi size: 1078395 label_size:  977824\n",
            "PADDED\n",
            "> DATA: Loaded scan IBSR_01 roi size: 1147470 label_size:  1093813\n",
            "PADDED\n",
            "> DATA: Loaded scan IBSR_03 roi size: 951358 label_size:  899576\n",
            "PADDED\n",
            "> DATA: Loaded scan IBSR_08 roi size: 868943 label_size:  787260\n",
            "Original samples:   2172 , Resampled  :  7752\n",
            "> DATA: Training sample size: 7752\n",
            "_________________________________________________\n",
            "Validation data: \n",
            "PADDED\n",
            "> DATA: Loaded scan IBSR_16 roi size: 1460808 label_size:  1385945\n",
            "PADDED\n",
            "> DATA: Loaded scan IBSR_07 roi size: 867764 label_size:  799514\n",
            "Original samples:   570 , Resampled  :  570\n",
            "> DATA: Training sample size: 570\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z2td8ejLRRaz",
        "colab_type": "text"
      },
      "source": [
        "Now we will get the data ratio to handle the imbalance of the dataset. We will calculate the ratio of data and a corresponding weight that will be used to train the model. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UoZZ5agIHn4Y",
        "colab_type": "code",
        "outputId": "9794b6f3-aa36-403b-e5c2-bf4e52ed8589",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "ratio = find_ratio(training_dataset)\n",
        "weights = 1 / ratio\n",
        "print('Class ratio  : ', ratio)\n",
        "print('Class weights: ', weights)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Class ratio  :  [0.35426724 0.1188033  0.29632398 0.23060548]\n",
            "Class weights:  [2.82272782 8.41727495 3.37468471 4.33641031]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FBMwKJVH13OB",
        "colab_type": "text"
      },
      "source": [
        "# 4.Build the U-Net Model and Train\n",
        "\n",
        "Now that the data to feed the network is ready, we will initialize the U-Net model and we will start training it"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4uFSBgrpBjKO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = UnetModel(in_chans=1, out_chans=4, chans=64, num_pool_layers=3, drop_prob=0.1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4MCD-wur2v-D",
        "colab_type": "text"
      },
      "source": [
        "Now we will set up some hyperparameters options such as:\n",
        "\n",
        "\n",
        "\n",
        "*   Number of Epochs\n",
        "*   Patience for Early Stopping\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "00dcAKaU2c-F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "options['gpu_use'] = True\n",
        "options['num_epochs'] = 200\n",
        "options['model_name'] = 'unet_tissue_segmentation'\n",
        "options['save_path'] = 'models' #'/content/gdrive/My Drive/misa_model_41/'\n",
        "options['patience'] = 20\n",
        "options['weights'] = weights\n",
        "options['lr'] = 0.0001"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hg75i18i-Z1y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir models"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VlHmMFGEIXQ6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#model.load_state_dict(torch.load('checkpoint.pt'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1PrRzr0vqZxJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "97078ff5-7949-4b46-d985-faa24dfe1751"
      },
      "source": [
        "options['gpu_use']"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YpRZn-Ea3G8H",
        "colab_type": "code",
        "outputId": "d3f4978e-3cff-4724-f7ec-873e06949612",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "options['lr'] = 0.0001\n",
        "train_model(model, options, train ing_dataloader, validation_dataloader)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Weights not loaded.\n",
            "Epoch 1 train_loss -1.8230 train_acc 0.6654 val_loss -2.3557 val_acc 0.8209 train_dice tensor([0.5463, 0.6811, 0.7038, 0.5941], device='cuda:0') val_dice tensor([0.7142, 0.8125, 0.7938, 0.8742], device='cuda:0')\n",
            "Validation loss decreased (inf --> -2.355736).  Saving model ...\n",
            "Epoch 2 train_loss -2.6405 train_acc 0.8311 val_loss -2.6044 val_acc 0.8728 train_dice tensor([0.7766, 0.8292, 0.8286, 0.8473], device='cuda:0') val_dice tensor([0.7973, 0.8714, 0.8294, 0.9278], device='cuda:0')\n",
            "Validation loss decreased (-2.355736 --> -2.604373).  Saving model ...\n",
            "Epoch 3 train_loss -2.8597 train_acc 0.8692 val_loss -2.6815 val_acc 0.8939 train_dice tensor([0.8401, 0.8661, 0.8603, 0.9002], device='cuda:0') val_dice tensor([0.8344, 0.8886, 0.8599, 0.9516], device='cuda:0')\n",
            "Validation loss decreased (-2.604373 --> -2.681550).  Saving model ...\n",
            "Epoch 4 train_loss -2.9358 train_acc 0.8840 val_loss -2.7218 val_acc 0.9047 train_dice tensor([0.8625, 0.8802, 0.8760, 0.9143], device='cuda:0') val_dice tensor([0.8526, 0.9004, 0.8732, 0.9553], device='cuda:0')\n",
            "Validation loss decreased (-2.681550 --> -2.721830).  Saving model ...\n",
            "Epoch 5 train_loss -2.9595 train_acc 0.8869 val_loss -2.7414 val_acc 0.9066 train_dice tensor([0.8745, 0.8824, 0.8790, 0.9178], device='cuda:0') val_dice tensor([0.8629, 0.9022, 0.8766, 0.9532], device='cuda:0')\n",
            "Validation loss decreased (-2.721830 --> -2.741431).  Saving model ...\n",
            "Epoch 6 train_loss -3.0163 train_acc 0.8988 val_loss -2.7554 val_acc 0.9129 train_dice tensor([0.8942, 0.8943, 0.8921, 0.9252], device='cuda:0') val_dice tensor([0.8716, 0.9089, 0.8848, 0.9581], device='cuda:0')\n",
            "Validation loss decreased (-2.741431 --> -2.755373).  Saving model ...\n",
            "Epoch 7 train_loss -3.0051 train_acc 0.8979 val_loss -2.7263 val_acc 0.9025 train_dice tensor([0.8868, 0.8936, 0.8921, 0.9229], device='cuda:0') val_dice tensor([0.8516, 0.8969, 0.8725, 0.9552], device='cuda:0')\n",
            "EarlyStopping counter: 1 out of 20\n",
            "Epoch 8 train_loss -3.0247 train_acc 0.9006 val_loss -2.7706 val_acc 0.9149 train_dice tensor([0.8946, 0.8958, 0.8950, 0.9262], device='cuda:0') val_dice tensor([0.8768, 0.9107, 0.8886, 0.9577], device='cuda:0')\n",
            "Validation loss decreased (-2.755373 --> -2.770551).  Saving model ...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Io2TxCe4Xzgl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#model.load_state_dict(torch.load('/content/gdrive/My Drive/misa_model_41/unet_tissue_segmentation71.pt')) \n",
        "device = torch.device('cuda') if options['gpu_use'] else torch.device('cpu')\n",
        "model = model.to(device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "75qruwqpAFIU",
        "colab_type": "text"
      },
      "source": [
        "# 5.Test the model\n",
        "\n",
        "Once we have trained our first `Unet` model, we can infer some testing cases to see how well it performs. We are  going to use some additional helper functions included in `utils.py` that will help us to extract the image patches to infer the model:\n",
        "\n",
        "* `get_inference_patches` will extract $[num_{samples}, 2, 32, 32, 32]$ patches from the input image along with their coordenates $(x,y,z)$. \n",
        "\n",
        "* `reconstruct_image` will take the predicted patches and reconstruct them back as an image using the coordinates as a  base. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aotQYX7I3lv7",
        "colab_type": "code",
        "outputId": "54093ec2-8487-4cc2-e73f-b4ed7e675dd0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        }
      },
      "source": [
        "test_samples = list(input_dictionary['input_val_data'].keys())\n",
        "scan_path = os.path.join(options['val_path'], test_samples[0])\n",
        "\n",
        "# Visualize the image\n",
        "scan = ants.image_read(os.path.join(scan_path, test_samples[0] + \".nii.gz\"))\n",
        "scan.plot(nslices=9, axis=2, title='Subject: '+test_samples[0] )"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qgM51yDxMVkzTiwl02zgypMzKay5y+hdELPipVCo9xTxEh6BpoDLNg2hJIS1L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\nB6x0nrxG3WlORpLnmkgkhA3VpXGepy578BpZ9nOyLLwW/tTrdbTbbay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ZLA4ePIhoNIpw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\nLLA0DMMwDMMwOoIFloZhGIZhGEZH+D+KeFExZ6o44wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x432 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yvIo2IZ2HkpQ",
        "colab_type": "text"
      },
      "source": [
        "Now we will evaluate the segmentation result on the presented Subject, \n",
        "First we will show the result of the segmentation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L4YT12KrILOS",
        "colab_type": "code",
        "outputId": "569d8069-a691-44db-f19f-cfcb5e9f728b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        }
      },
      "source": [
        "segm_img = evaluate_subject(scan_path, test_samples[0], options, model)\n",
        "segm_img.plot(nslices=9, axis=2, title='Subject: '+test_samples[0] )"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/UNET/utils/utils.py:499: RuntimeWarning: invalid value encountered in true_divide\n",
            "  out_image /= freq_count\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pHJeOnaKSJYCRgmZszcv9PDZ848LlwW0rttfet5YsE2PIRSPZi23Y2KsXQ49L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\nABUklgAAAFBBYgkAAAAV/x+FQVpsLbiMigAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x432 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F5aoZdLXU1KE",
        "colab_type": "text"
      },
      "source": [
        "##Dice coefficients calculation for the Validation Set\n",
        "\n",
        "Now we will evaluate the segmentation in all the images of the validation dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWV5QCwsUVIi",
        "colab_type": "code",
        "outputId": "5669410d-e785-4878-9c05-b1fd0724ed7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        }
      },
      "source": [
        "# Join all the volumes to segment:\n",
        "volumes_to_evaluate = {}\n",
        "volumes_to_evaluate.update(input_dictionary['input_val_data'])\n",
        "\n",
        "# Evaluate on all the samples:\n",
        "test_samples = list(volumes_to_evaluate.keys())\n",
        "evaluate_set(test_samples, options, model)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation volume:  IBSR_16\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/UNET/utils/utils.py:499: RuntimeWarning: invalid value encountered in true_divide\n",
            "  out_image /= freq_count\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Evaluation volume:  IBSR_07\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CSF</th>\n",
              "      <th>GM</th>\n",
              "      <th>WM</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>IBSR_16</th>\n",
              "      <td>0.859865</td>\n",
              "      <td>0.924257</td>\n",
              "      <td>0.891328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IBSR_07</th>\n",
              "      <td>0.914853</td>\n",
              "      <td>0.920505</td>\n",
              "      <td>0.907135</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              CSF        GM        WM\n",
              "IBSR_16  0.859865  0.924257  0.891328\n",
              "IBSR_07  0.914853  0.920505  0.907135"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ruvnjySuQkZT",
        "colab_type": "code",
        "outputId": "02e23c20-d143-44a2-9093-30c34d4bda45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "test_samples"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['IBSR_16', 'IBSR_07']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tNavKMhUQbdi",
        "colab_type": "text"
      },
      "source": [
        "# GENERATE MASKS:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PxorTK1xQu4v",
        "colab_type": "code",
        "outputId": "e0cb605d-1f78-45b1-8c7c-659cfd4fd144",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "# Join all the volumes to segment:\n",
        "volumes_to_evaluate = {}\n",
        "volumes_to_evaluate.update(input_dictionary['input_test_data'])\n",
        "\n",
        "# Evaluate on all the samples:\n",
        "test_samples = list(volumes_to_evaluate.keys())\n",
        "dice_scores, avd_scores, hud_scores = evaluate_test_set(test_samples, options, model)\n",
        "dice_scores"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation volume:  IBSR_11\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/UNET/utils/utils.py:499: RuntimeWarning: invalid value encountered in true_divide\n",
            "  out_image /= freq_count\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Evaluation volume:  IBSR_13\n",
            "Evaluation volume:  IBSR_17\n",
            "Evaluation volume:  IBSR_14\n",
            "Evaluation volume:  IBSR_12\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CSF</th>\n",
              "      <th>GM</th>\n",
              "      <th>WM</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>IBSR_11</th>\n",
              "      <td>0.912701</td>\n",
              "      <td>0.926566</td>\n",
              "      <td>0.925557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IBSR_13</th>\n",
              "      <td>0.902323</td>\n",
              "      <td>0.926438</td>\n",
              "      <td>0.899622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IBSR_17</th>\n",
              "      <td>0.939298</td>\n",
              "      <td>0.927427</td>\n",
              "      <td>0.902453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IBSR_14</th>\n",
              "      <td>0.920844</td>\n",
              "      <td>0.941384</td>\n",
              "      <td>0.926799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IBSR_12</th>\n",
              "      <td>0.875420</td>\n",
              "      <td>0.920959</td>\n",
              "      <td>0.922149</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              CSF        GM        WM\n",
              "IBSR_11  0.912701  0.926566  0.925557\n",
              "IBSR_13  0.902323  0.926438  0.899622\n",
              "IBSR_17  0.939298  0.927427  0.902453\n",
              "IBSR_14  0.920844  0.941384  0.926799\n",
              "IBSR_12  0.875420  0.920959  0.922149"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D0EffFabjClS",
        "colab_type": "code",
        "outputId": "26b771e5-5299-4ab9-9586-03538436b199",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "avd_scores"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CSF</th>\n",
              "      <th>GM</th>\n",
              "      <th>WM</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>IBSR_11</th>\n",
              "      <td>0.073257</td>\n",
              "      <td>0.043757</td>\n",
              "      <td>0.076545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IBSR_13</th>\n",
              "      <td>0.020458</td>\n",
              "      <td>0.075211</td>\n",
              "      <td>0.093606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IBSR_17</th>\n",
              "      <td>0.018794</td>\n",
              "      <td>0.056934</td>\n",
              "      <td>0.077720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IBSR_14</th>\n",
              "      <td>0.082606</td>\n",
              "      <td>0.009131</td>\n",
              "      <td>0.045467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IBSR_12</th>\n",
              "      <td>0.193523</td>\n",
              "      <td>0.015108</td>\n",
              "      <td>0.019255</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              CSF        GM        WM\n",
              "IBSR_11  0.073257  0.043757  0.076545\n",
              "IBSR_13  0.020458  0.075211  0.093606\n",
              "IBSR_17  0.018794  0.056934  0.077720\n",
              "IBSR_14  0.082606  0.009131  0.045467\n",
              "IBSR_12  0.193523  0.015108  0.019255"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gTR9u_B5tB8y",
        "colab_type": "code",
        "outputId": "f0fc301e-6cac-4877-e96f-f9b03e24976d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "hud_scores"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CSF</th>\n",
              "      <th>GM</th>\n",
              "      <th>WM</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>IBSR_11</th>\n",
              "      <td>20.736441</td>\n",
              "      <td>32.465366</td>\n",
              "      <td>31.717503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IBSR_13</th>\n",
              "      <td>29.427878</td>\n",
              "      <td>35.482390</td>\n",
              "      <td>32.062439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IBSR_17</th>\n",
              "      <td>25.159491</td>\n",
              "      <td>30.149627</td>\n",
              "      <td>30.133038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IBSR_14</th>\n",
              "      <td>21.863211</td>\n",
              "      <td>26.944387</td>\n",
              "      <td>27.531800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IBSR_12</th>\n",
              "      <td>21.307276</td>\n",
              "      <td>30.149627</td>\n",
              "      <td>31.559468</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               CSF         GM         WM\n",
              "IBSR_11  20.736441  32.465366  31.717503\n",
              "IBSR_13  29.427878  35.482390  32.062439\n",
              "IBSR_17  25.159491  30.149627  30.133038\n",
              "IBSR_14  21.863211  26.944387  27.531800\n",
              "IBSR_12  21.307276  30.149627  31.559468"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e7WK-lEpmydc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        },
        "outputId": "6bfd00f0-408c-48fd-e3ee-8d2accb0e73d"
      },
      "source": [
        "!zip  -r results_validation.zip /content/data/dataset/Test_Set"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  adding: content/data/dataset/Test_Set/ (stored 0%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_11/ (stored 0%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_11/IBSR_11_norm.nii.gz (deflated 2%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_11/IBSR_11_ROI.nii.gz (deflated 47%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_11/IBSR_11_seg.nii.gz (deflated 11%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_11/IBSR_11_result.nii.gz (deflated 6%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_11/IBSR_11.nii.gz (deflated 1%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_13/ (stored 0%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_13/IBSR_13_ROI.nii.gz (deflated 49%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_13/IBSR_13_result.nii.gz (deflated 6%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_13/IBSR_13_seg.nii.gz (deflated 11%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_13/IBSR_13.nii.gz (deflated 2%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_13/IBSR_13_norm.nii.gz (deflated 2%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_17/ (stored 0%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_17/IBSR_17.nii.gz (deflated 1%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_17/IBSR_17_seg.nii.gz (deflated 8%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_17/IBSR_17_norm.nii.gz (deflated 1%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_17/IBSR_17_result.nii.gz (deflated 4%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_17/IBSR_17_ROI.nii.gz (deflated 46%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_14/ (stored 0%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_14/IBSR_14_seg.nii.gz (deflated 9%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_14/IBSR_14_norm.nii.gz (deflated 2%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_14/IBSR_14_result.nii.gz (deflated 5%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_14/IBSR_14_ROI.nii.gz (deflated 47%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_14/IBSR_14.nii.gz (deflated 2%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_12/ (stored 0%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_12/IBSR_12.nii.gz (deflated 2%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_12/IBSR_12_seg.nii.gz (deflated 11%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_12/IBSR_12_result.nii.gz (deflated 6%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_12/IBSR_12_norm.nii.gz (deflated 2%)\n",
            "  adding: content/data/dataset/Test_Set/IBSR_12/IBSR_12_ROI.nii.gz (deflated 46%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VKlgL7a30JJk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}