EM_stellar.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/akhadangi/EM-stellar/blob/master/EM_stellar.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3-2imw3fC9Ru",
        "colab_type": "text"
      },
      "source": [
        "# **EM-stellar**\n",
        "\n",
        "<font size = 4>This notebook represents the implementation of the [**EM-stellar**](https://www.biorxiv.org/content/10.1101/2020.07.15.203836v1). Deep learning (DL) methods offer an exciting opportunity to automate the segmentation process by learning from manual annotations of a small sample of EM data. While many DL methods are being rapidly adopted to segment EM data no benchmark analysis has been conducted on these methods to date. We present EM-stellar as a platform that can be used to benchmark the performance of a range of state-of-the-art DL methods on user-provided datasets. \n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "<font size = 4>Papers related to this Notebook: \n",
        "\n",
        "- <font size = 3>**EM-stellar: benchmarking deep learning for electron microscopy image segmentation** by *Afshin Khadangi, Thomas Boudier, Vijay Rajagopal*  (https://www.biorxiv.org/content/10.1101/2020.07.15.203836v1)\n",
        "\n",
        "- <font size = 3>**EM-net: Deep learning for electron microscopy image segmentation** by *Afshin Khadangi, Thomas Boudier, Vijay Rajagopal*  (https://www.biorxiv.org/content/10.1101/2020.02.03.933127v1)\n",
        "\n",
        "\n",
        "<font size = 4>**Please cite** [**this original paper**](https://www.biorxiv.org/content/10.1101/2020.07.15.203836v1) **when using or developing this notebook.**\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VIou2bhqDHNI",
        "colab_type": "text"
      },
      "source": [
        "###**Structure of a notebook**\n",
        "\n",
        "<font size = 2> This cell is adapted from the [ZeroCostDL4Mic](https://github.com/HenriquesLab/ZeroCostDL4Mic).\n",
        "\n",
        "---\n",
        "<font size = 4>The notebook contains two types of cell:  \n",
        "\n",
        "<font size = 4>**Text cells** provide information and can be modified by douple-clicking the cell. You are currently reading the text cell. You can create a new text by clicking `+ Text`.\n",
        "\n",
        "<font size = 4>**Code cells** contain code and the code can be modfied by selecting the cell. To execute the cell, move your cursor on the `[ ]`-mark on the left side of the cell (play button appears). Click to execute the cell. After execution is done the animation of play button stops. You can create a new coding cell by clicking `+ Code`.\n",
        "\n",
        "---\n",
        "###**Table of contents, Code snippets** and **Files**\n",
        "\n",
        "<font size = 4>On the top left side of the notebook you find three tabs which contain from top to bottom:\n",
        "\n",
        "<font size = 4>*Table of contents* = contains structure of the notebook. Click the content to move quickly between sections.\n",
        "\n",
        "<font size = 4>*Code snippets* = contain examples how to code certain tasks. You can ignore this when using this notebook.\n",
        "\n",
        "<font size = 4>*Files* = contain all available files. After mounting your google drive (see section 1.) you will find your files and folders here. \n",
        "\n",
        "<font size = 4>**Remember that all uploaded files are purged after changing the runtime.** All files saved in Google Drive will remain. You do not need to use the Mount Drive-button; your Google Drive is connected in section 1.2.\n",
        "\n",
        "<font size = 4>**Note:** The \"sample data\" in \"Files\" contains default files. Do not upload anything in here!\n",
        "\n",
        "---\n",
        "###**Making changes to the notebook**\n",
        "\n",
        "<font size = 4>**You can make a copy** of the notebook and save it to your Google Drive. To do this click file -> save a copy in drive.\n",
        "\n",
        "<font size = 4>To **edit a cell**, double click on the text. This will show you either the source code (in code cells) or the source text (in text cells).\n",
        "You can use the `#`-mark in code cells to comment out parts of the code. This allows you to keep the original code piece in the cell as a comment."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mhAmFUGeDM2l",
        "colab_type": "text"
      },
      "source": [
        "# **0. Before getting started**\n",
        "---\n",
        "\n",
        "<font size = 3> **Important:** Make sure that all of the images are equal in size, both in $x$ and $y$. For example, suppose that you have segmented your test data with [**EM-net**](https://github.com/akhadangi/EM-net) by trying a variety of the networks, and you aim at benchmarking these methods by retrieving a wide range of evaluation metrics. Let's say that you have tried segmenting your test images on U-net, VGG and ResNet with the following image sizes:\n",
        "*   $256$ x $512$\n",
        "\n",
        "<font size = 3>You have also previously tried EM-net v1 and v2 2x with the following image sizes: \n",
        "*   $512$ x $512$\n",
        "\n",
        "<font size = 3>Supposing these different patches have overlapping areas, then you will need to extract those areas before using EM-stellar. Let's suppose that these two different datasets share an area of $128$ x $128$. Hence you will need to extract these overlapping patches before using this platform.\n",
        "\n",
        "<font size = 3> **Important:** Make sure that you have saved your patches in the ***.png** format.\n",
        "0. Create a folder called \"Main\".\n",
        "1. Create two sub folders `test_segmentation_results` and `ground_truth_segmentation_masks` within the \"Main\" folder in the following format and upload them into your google drive:\n",
        "\n",
        "<span style=\"color:blue\">some *This is Blue italic.* text</span>\n",
        "\n",
        "*   **drive**\n",
        "    - **Main**\n",
        "      - ***test_segmentation_results***\n",
        "        - U-net\n",
        "          - *result_1.png*, *result_2.png*, ...\n",
        "        - VGG\n",
        "          - *result_1.png*, *result_2.png*, ...\n",
        "        - EM-net v1 BN\n",
        "          - *result_1.png*, *result_2.png*, ...\n",
        "        - ResNet\n",
        "          - *result_1.png*, *result_2.png*, ...\n",
        "        - ...\n",
        "      - ***ground_truth_segmentation_masks***\n",
        "        - *mask_1.png*, *mask_2.png*, ...\n",
        "    \n",
        "\n",
        "2. Place the segmentation result images (e.g. predicted by the networks) in the corresponding folder (for example folder `u-net`) and put all these folders to **test_segmentation_results** folder.\n",
        "3. Place all your ground-truth images in the **ground_truth_segmentation_masks** folder. This folder contains the binary masks that we want to compare the predictions of the methods with. Hence, it is different from the validation data as the validation data is sampled from the training data that you have already performed during the training.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HKl_tuYrDaAK",
        "colab_type": "text"
      },
      "source": [
        "## **1.1 Change the Runtime**\n",
        "---\n",
        "\n",
        "<font size = 4>Go to **Runtime -> Change the Runtime type**\n",
        "\n",
        "<font size = 4>**Runtime type: Python 3** *(Python 3 is programming language in which this program is written)*\n",
        "\n",
        "<font size = 4>**Accelator:** *This notebook is accelerator free! Select the `None` as type of the accelerator.*\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6oT6PU0WDyo5",
        "colab_type": "text"
      },
      "source": [
        "## **1.2. Mount your Google Drive**\n",
        "---\n",
        "<font size = 4> To use this notebook on the data present in your Google Drive, you need to mount your Google Drive to this notebook.\n",
        "\n",
        "<font size = 4> Play the cell below to mount your Google Drive and follow the link. In the new browser window, select your drive and select 'Allow', copy the code, paste into the cell and press enter. This will give Colab access to the data on the drive. \n",
        "\n",
        "<font size = 4> Once this is done, your data are available in the **Files** tab on the top left of notebook."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2mVod4MTD2Kf",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "ced444a4-b538-43d9-e041-096d743785aa"
      },
      "source": [
        "#@markdown ##Play the cell to connect your Google Drive to Colab\n",
        "\n",
        "#@markdown * Click on the URL. \n",
        "\n",
        "#@markdown * Sign in your Google Account. \n",
        "\n",
        "#@markdown * Copy the authorization code. \n",
        "\n",
        "#@markdown * Enter the authorization code. \n",
        "\n",
        "#@markdown * Click on \"Files\" site on the right. Refresh the site. Your Google Drive folder should now be available here as \"drive\". \n",
        "\n",
        "# mount user's Google Drive to Google Colab.\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "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": "-hRKMfzsD9Nf",
        "colab_type": "text"
      },
      "source": [
        "# **2. Install EM-stellar Dependencies**\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1KynBS5y1mY3",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "%%capture\n",
        "#@markdown ##Play to install EM-stellar standard dependencies\n",
        "# import logging, os\n",
        "\n",
        "# logging.disable(logging.WARNING)\n",
        "\n",
        "# importing os to set environment variable\n",
        "import os\n",
        "!pip install tqdm\n",
        "\n",
        "# install openjdk for java and set ENV variable\n",
        "!apt-get install -y openjdk-8-jdk-headless -qq > /dev/null\n",
        "os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"     #set environment variable\n",
        "print(\"1/7: Installed Java openjdk 8.\")\n",
        "# install maven\n",
        "!sudo apt-get install maven\n",
        "print(\"2/7: Installed maven.\")\n",
        "\n",
        "# install imglyb\n",
        "!pip install imglyb==0.3.5\n",
        "print(\"3/7: Installed imglyb.\")\n",
        "\n",
        "# install pyimagej\n",
        "!pip install pyimagej\n",
        "print(\"4/7: Installed pyimagej (for FiJi use).\")\n",
        "\n",
        "# install pyjnius (python library to access java classes)\n",
        "!wget https://anaconda.org/conda-forge/pyjnius/1.2.0/download/linux-64/pyjnius-1.2.0-py36hc2cfd8d_0.tar.bz2 && tar -xjf pyjnius-1.2.0-py36hc2cfd8d_0.tar.bz2 -C /usr/local\n",
        "print(\"5/7: Installed pyjnius (python wrapper for Java).\")\n",
        "\n",
        "print(\"Downloading latest version of FiJi ...\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q8wQlT38C9IE",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "%%capture\n",
        "%%bash\n",
        "#@markdown ##Play to install FiJi (ImageJ)\n",
        "\n",
        "wget https://downloads.imagej.net/fiji/latest/fiji-linux64.zip && unzip fiji-linux64.zip\n",
        "\n",
        "\n",
        "cd Fiji.app/\n",
        "./ImageJ-linux64 --update add-update-site clij2 https://sites.imagej.net/clij2/\n",
        "./ImageJ-linux64 --update add-update-site clij https://sites.imagej.net/clij/\n",
        "./ImageJ-linux64 --update update"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CpF4ndOTEpK7",
        "colab_type": "text"
      },
      "source": [
        "# **3. Select your paths**\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X8s9bGkyE1EI",
        "colab_type": "text"
      },
      "source": [
        "<font size = 5> **Paths for segmentation results and ground-truth binary masks**\n",
        "\n",
        "<font size = 4>**`segmentation_results`, `ground_truth_binary_masks`:** These are the folders containing your segmentation results (e.g. predictions from networks) and ground-truth binary masks files (manually annotated masks). Enter the path to the source and target images for training. **These should be located in the same parent folder.**\n",
        "\n",
        "<font size = 4>**Make sure the directories exist before entering them!**\n",
        "\n",
        "<font size = 5> **Select benchmarking metrics**\n",
        "\n",
        "<font size = 4>**metrics**: choose the metrics that you want to use for benchmarking different networks that you have used for segmenting test data.\n",
        "\n",
        "---\n",
        "<font size = 4>**Caution!** If you run this notebook **for the first time**, you will need to run the following cell twice as some dependency issues of ImageJ with the Colab. Run the following cell and if you encounter an error, please Go to **Runtime -> Restart runtime**. Once it has been restarted, please run the following cell again until you see the output `FiJi has been loaded successfully!`.\n",
        "You won't need to re-run the previous cells after restarting the runtime as they have been already cached on the Colab.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z5CbeGvVFPM8",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "outputId": "edb90ff6-8f5e-4f24-c083-047dacef74b5"
      },
      "source": [
        "#@markdown ###Path to training images:\n",
        "# import imagej\n",
        "import os\n",
        "os.environ[\"PYJNIUS_JAR\"] = \"/usr/local/share/pyjnius/pyjnius.jar\"\n",
        "import imagej;\n",
        "ij = imagej.init(\"Fiji.app\");\n",
        "print('FiJi has been loaded successfully!')\n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive', force_remount=True)\n",
        "import os\n",
        "from pathlib import Path\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.metrics import confusion_matrix, jaccard_score\n",
        "from skimage.io import imread_collection, imsave\n",
        "import glob\n",
        "from PIL import Image\n",
        "import seaborn as sns\n",
        "\n",
        "import shutil\n",
        "segmentation_results = '/content/gdrive/My Drive/Images/Networks outputs' #@param {type:\"string\"}\n",
        "\n",
        "#We find the name of the base_directory\n",
        "path = Path(segmentation_results)\n",
        "base_directory = '/'+(str(path.parent))\n",
        "\n",
        "ground_truth_binary_masks = '/content/gdrive/My Drive/Images/Groundtruth' #@param {type:\"string\"}\n",
        "#We assign a temporary directory to just include the stacked images\n",
        "Temp_dir = base_directory + '/Temp'\n",
        "\n",
        "#Cleaning up pre-existing directories, if any!\n",
        "if os.path.exists(Temp_dir):\n",
        "  shutil.rmtree(Temp_dir)\n",
        "\n",
        "#Creating new directories\n",
        "os.mkdir(Temp_dir)\n",
        "\n",
        "\n",
        "#@markdown ###Select the metrics that you want to evaluate inference results with:\n",
        "\n",
        "V_Rand = True #@param {type:\"boolean\"}\n",
        "V_Info = True #@param {type:\"boolean\"}\n",
        "Standard_metrics = True #@param {type:\"boolean\"}"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "FiJi has been loaded successfully!\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aArhE68kIsK6",
        "colab_type": "text"
      },
      "source": [
        "# **4. Visualise the metrics**\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hva4-GVyJKpF",
        "colab_type": "text"
      },
      "source": [
        "<font size = 5> **Paths for training data and models**\n",
        "\n",
        "<font size = 4>**`Training_source`, `Training_target`:** These are the folders containing your source (e.g. EM images) and target files (segmentation masks). Enter the path to the source and target images for training. **These should be located in the same parent folder.**\n",
        "\n",
        "<font size = 4>**`Patch_width`, `Patch_height`:** Training deep neural networks requires lots of computaional resources including **memory**. Feeding larger image data size to the network can exhaust the resources. Such a limitation can be handled by reducing the input image size. Hence, we have provided **patch training** which enables the user to overcome this limitation. You can simply upload the training images with any size, and set the patch size by controlling these two parameters. **The larger patch size will require higher memory.**\n",
        "\n",
        "<font size = 4>**`model_name`:** Use only my_model -style, not my-model. If you want to use a previously trained model, enter the name of the pretrained model (which should be contained in the trained_model -folder after training).\n",
        "\n",
        "<font size = 4>**`model_path`**: Enter the path of the folder where you want to save your model.\n",
        "\n",
        "**Make sure the directories exist before entering them!**\n",
        "\n",
        "<font size = 5> **Select training parameters**\n",
        "\n",
        "<font size = 4>**`epochs`**: Choose more epochs for larger training sets. Observing how much the loss reduces between epochs during training may help determine the optimal value. **Default: 50**\n",
        "\n",
        "<font size = 5>**Advanced Parameters - experienced users only**\n",
        "\n",
        "<font size = 4>**`batch_size`**: This parameter describes the amount of images that are loaded into the network per step. Smaller batchsizes may improve training performance slightly but may increase training time. If the notebook crashes while loading the dataset this can be due to a too large batch size. Decrease the number in this case. **Default: 2**\n",
        "\n",
        "<font size = 4>**`loss_functions`**: Training performance depends strongly on the loss function. To find out more about losses, see: https://keras.io/losses/. **Default: binary_crossentropy**\n",
        "\n",
        "<font size = 4>**`optimizer`**: Training performance depends strongly on the optimiser as well. To find out more about optimizers, see: https://keras.io/optimizers/. **Default: Rectified Adam**\n",
        "\n",
        "\n"
      ]
    },
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        "id": "8OyqdazhKKN_",
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        },
        "outputId": "80588213-fbb4-4bee-c644-aba08d959813"
      },
      "source": [
        "#@markdown ##4.1. Play to retrieve the evaluation metrics values\n",
        "import logging, os\n",
        "from tqdm.autonotebook import tqdm\n",
        "\n",
        "for root, dirs, files in os.walk(segmentation_results, topdown=True):\n",
        "  if (len(dirs)>0):\n",
        "    childdirs = dirs\n",
        "parentdir = [ground_truth_binary_masks, segmentation_results]\n",
        "\n",
        "childdirs_imgs = []\n",
        "print(\"iterating the segmentation results directories ...\")\n",
        "for item in tqdm(childdirs):\n",
        "    tmp = np.asarray(list(imread_collection(parentdir[1]+\"/\"+item+\"/*.png\")),dtype='float32')\n",
        "    if (np.max(tmp)>1):tmp=tmp/255.0\n",
        "    childdirs_imgs.append(tmp)\n",
        "    tmp = np.vstack((tmp,tmp))\n",
        "    imsave(Temp_dir+'/'+item+'.tif',tmp)\n",
        "Ground_data = np.asarray(list(imread_collection(parentdir[0]+\"/*.png\")),dtype='float32')\n",
        "Ground_dataTmp = np.vstack((Ground_data,Ground_data))\n",
        "zipdata = zip(childdirs, childdirs_imgs)\n",
        "mydict = dict(zipdata)\n",
        "if (np.max(Ground_data)>1):Ground_data=Ground_data/255.0\n",
        "if (np.max(Ground_dataTmp)>1):Ground_dataTmp=Ground_dataTmp/255.0\n",
        "imsave(Temp_dir+'/Ground.tif',Ground_dataTmp)\n",
        "\n",
        "\n",
        "Language_extension = \"BeanShell\"\n",
        "\n",
        "macroVRand = \"\"\"\n",
        "import trainableSegmentation.metrics.*;\n",
        "#@output String VRand\n",
        "metric = new RandError( originalLabels, proposedLabels );\n",
        "maxThres = 1.0;\n",
        "maxScore = metric.getMaximalVRandAfterThinning( 0.0, maxThres, 0.1, true );  \n",
        "VRand = maxScore;\n",
        "\"\"\"\n",
        "\n",
        "macroVInfo = \"\"\"\n",
        "import trainableSegmentation.metrics.*;\n",
        "#@output String VInfo\n",
        "metric = new VariationOfInformation( originalLabels, proposedLabels );\n",
        "maxThres =1.0;\n",
        "maxScore = metric.getMaximalVInfoAfterThinning( 0.0, maxThres, 0.1 );  \n",
        "VInfo = maxScore;\n",
        "\"\"\"\n",
        "current_path = Temp_dir\n",
        "VRandDf = []\n",
        "VInfoDf = []\n",
        "if (V_Rand or V_Info):\n",
        "  print(\"Retrieving Rand/Info metrics for the networks ...\")\n",
        "for item in tqdm(childdirs):\n",
        "  if (V_Rand):\n",
        "    macroVRandTmp = ('\\nimport ij.IJ;\\noriginalLabels=IJ.openImage(\"' + current_path + '/' + 'Ground' + '.tif\");' +\n",
        "                  '\\nproposedLabels=IJ.openImage(\"' + current_path + '/' + item + '.tif\");' + macroVRand)\n",
        "    VRand = float(ij.py.run_script(Language_extension, macroVRandTmp).getOutput('VRand'))\n",
        "    VRandDf.append(VRand)\n",
        "  if (V_Info):\n",
        "    macroVInfoTmp = ('\\nimport ij.IJ;\\noriginalLabels=IJ.openImage(\"' + current_path + '/' + 'Ground' + '.tif\");' +\n",
        "                  '\\nproposedLabels=IJ.openImage(\"' + current_path + '/' + item + '.tif\");' + macroVInfo)\n",
        "    VInfo = float(ij.py.run_script(Language_extension, macroVInfoTmp).getOutput('VInfo'))\n",
        "    VInfoDf.append(VInfo)\n",
        "\n",
        "  \n",
        "if (Standard_metrics):\n",
        "  print(\"Retrieving standard metrics for the segmentation results ...\")\n",
        "  Ground = Ground_data.flatten()\n",
        "  Ground = (Ground > 0.5).astype(np.int_)\n",
        "  F1score = []\n",
        "  accuracy = []\n",
        "  sensitivity = []\n",
        "  specificity = []\n",
        "  PPV = []\n",
        "  NPV = []\n",
        "  Jaccard = []\n",
        "  for item in tqdm(mydict):\n",
        "      itemTmp = mydict[item]\n",
        "      itemTmp = (itemTmp > 0.5).astype(np.int_)\n",
        "      itemTmp = itemTmp.flatten()\n",
        "      tn, fp, fn, tp = confusion_matrix(Ground, itemTmp).ravel()\n",
        "      accuracy.append((tp+tn)/(tp+tn+fp+fn))\n",
        "      sn = tp/(tp+fn)\n",
        "      sensitivity.append(sn)\n",
        "      specificity.append(tn/(tn+fp))\n",
        "      pr = tp/(tp+fp)\n",
        "      PPV.append(pr)\n",
        "      NPV.append(tn/(tn+fn))\n",
        "      Jaccard.append(jaccard_score(Ground, itemTmp))\n",
        "      F1score.append(2*(pr*sn)/(pr+sn))    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "iterating the segmentation results directories ...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9dea6b11df9a4dd1ad75c19f3217c8a8",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=13.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Retrieving Rand/Info metrics for the networks ...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Retrieving standard metrics for the segmentation results ...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iuvA_7EYK24I",
        "colab_type": "text"
      },
      "source": [
        "## **4.2. Visualisation**\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zovmk1wDJQzl",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "3eda4adc-d90b-478e-c2d1-a0042917a6a0"
      },
      "source": [
        "#@markdown ###4.2.1 Play to visualise the tabular report\n",
        "import logging, os\n",
        "if (V_Rand and V_Info and Standard_metrics):\n",
        "  MyDataFrameDict = {'Network': childdirs,\n",
        "          'F1-score': F1score,\n",
        "          'VRand': VRandDf,\n",
        "          'VInfo': VInfoDf,\n",
        "          'accuracy': accuracy,\n",
        "          'sensitivity': sensitivity,\n",
        "          'specificity': specificity,\n",
        "          'PPV': PPV,\n",
        "          'NPV': NPV,\n",
        "          'Jaccard': Jaccard\n",
        "          }\n",
        "elif (V_Rand and V_Info):\n",
        "  MyDataFrameDict = {'Network': childdirs,\n",
        "          'VRand': VRandDf,\n",
        "          'VInfo': VInfoDf\n",
        "          }\n",
        "elif (V_Rand and Standard_metrics):\n",
        "  MyDataFrameDict = {'Network': childdirs,\n",
        "          'F1-score': F1score,\n",
        "          'VRand': VRandDf,\n",
        "          'accuracy': accuracy,\n",
        "          'sensitivity': sensitivity,\n",
        "          'specificity': specificity,\n",
        "          'PPV': PPV,\n",
        "          'NPV': NPV,\n",
        "          'Jaccard': Jaccard\n",
        "          }\n",
        "elif (V_Info and Standard_metrics):\n",
        "  MyDataFrameDict = {'Network': childdirs,\n",
        "          'F1-score': F1score,\n",
        "          'VInfo': VInfoDf,\n",
        "          'accuracy': accuracy,\n",
        "          'sensitivity': sensitivity,\n",
        "          'specificity': specificity,\n",
        "          'PPV': PPV,\n",
        "          'NPV': NPV,\n",
        "          'Jaccard': Jaccard\n",
        "          }\n",
        "elif (Standard_metrics):\n",
        "  MyDataFrameDict = {'Network': childdirs,\n",
        "          'F1-score': F1score,\n",
        "          'accuracy': accuracy,\n",
        "          'sensitivity': sensitivity,\n",
        "          'specificity': specificity,\n",
        "          'PPV': PPV,\n",
        "          'NPV': NPV,\n",
        "          'Jaccard': Jaccard\n",
        "          }\n",
        "elif (V_Info):\n",
        "  MyDataFrameDict = {'Network': childdirs,\n",
        "          'VInfo': VInfoDf\n",
        "          }\n",
        "elif (V_Rand):\n",
        "  MyDataFrameDict = {'Network': childdirs,\n",
        "          'VRand': VRandDf\n",
        "          }\n",
        "\n",
        "df = pd.DataFrame(MyDataFrameDict, index=childdirs, columns = ['F1-score', 'VRand', 'VInfo', 'accuracy', 'sensitivity', \n",
        "                                                  'specificity', 'PPV', 'NPV', 'Jaccard'])\n",
        "df = df.round(decimals=5)\n",
        "\n",
        "cm = sns.diverging_palette(20, 130, n = len(childdirs), l=70, as_cmap=True)\n",
        "\n",
        "\n",
        "styles = [\n",
        "    dict(props=[(\"font-family\", \"Times New Roman\"),\n",
        "                               (\"text-align\", \"center\")])\n",
        "]\n",
        "pd.set_option('precision', 5)\n",
        "df.sort_index(axis=0, inplace = True)\n",
        "html = (df.style.set_table_styles(styles).background_gradient(cmap=cm))\n",
        "html"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "            background-color:  #f5bcb3;\n",
              "            color:  #000000;\n",
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              "            background-color:  #54c160;\n",
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              "            background-color:  #54c160;\n",
              "            color:  #000000;\n",
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              "            background-color:  #f5bdb3;\n",
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              "            background-color:  #d1eed4;\n",
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              "            background-color:  #76cd7f;\n",
              "            color:  #000000;\n",
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              "            background-color:  #86d38e;\n",
              "            color:  #000000;\n",
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              "            background-color:  #71cb7a;\n",
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              "            background-color:  #ebf8ec;\n",
              "            color:  #000000;\n",
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              "            background-color:  #6cc975;\n",
              "            color:  #000000;\n",
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              "            background-color:  #6cc975;\n",
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              "            background-color:  #ecf8ee;\n",
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              "            background-color:  #dff3e1;\n",
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              "            background-color:  #ef9383;\n",
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              "            background-color:  #ef9383;\n",
              "            color:  #000000;\n",
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              "            background-color:  #ef9383;\n",
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              "            background-color:  #ef9383;\n",
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              "            background-color:  #cfedd2;\n",
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              "            background-color:  #ef9383;\n",
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              "            background-color:  #dff3e1;\n",
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              "            background-color:  #94d89b;\n",
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              "            background-color:  #99daa0;\n",
              "            color:  #000000;\n",
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              "            background-color:  #e8f6e9;\n",
              "            color:  #000000;\n",
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              "            background-color:  #c3e9c7;\n",
              "            color:  #000000;\n",
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              "            background-color:  #f8d4ce;\n",
              "            color:  #000000;\n",
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              "            background-color:  #f8d0c9;\n",
              "            color:  #000000;\n",
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              "            background-color:  #c6eac9;\n",
              "            color:  #000000;\n",
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              "            background-color:  #e0f4e2;\n",
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              "            background-color:  #aae0b0;\n",
              "            color:  #000000;\n",
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              "            background-color:  #a2dda8;\n",
              "            color:  #000000;\n",
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              "            background-color:  #c2e9c6;\n",
              "            color:  #000000;\n",
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              "            background-color:  #aae0b0;\n",
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              "            background-color:  #fbe5e1;\n",
              "            color:  #000000;\n",
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              "            background-color:  #8ad492;\n",
              "            color:  #000000;\n",
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              "            background-color:  #8ed695;\n",
              "            color:  #000000;\n",
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              "            background-color:  #fbe4e1;\n",
              "            color:  #000000;\n",
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              "            background-color:  #ade1b2;\n",
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              "            background-color:  #b7e5bc;\n",
              "            color:  #000000;\n",
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              "            background-color:  #8cd594;\n",
              "            color:  #000000;\n",
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              "            background-color:  #ddf3e0;\n",
              "            color:  #000000;\n",
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              "            background-color:  #b6e4ba;\n",
              "            color:  #000000;\n",
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              "            background-color:  #f9dad5;\n",
              "            color:  #000000;\n",
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              "            background-color:  #7ed086;\n",
              "            color:  #000000;\n",
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              "            background-color:  #81d189;\n",
              "            color:  #000000;\n",
              "        }    #T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row11_col7 {\n",
              "            background-color:  #f9d9d4;\n",
              "            color:  #000000;\n",
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              "            background-color:  #b8e5bd;\n",
              "            color:  #000000;\n",
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              "            background-color:  #cfedd2;\n",
              "            color:  #000000;\n",
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              "            background-color:  #8cd594;\n",
              "            color:  #000000;\n",
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              "            background-color:  #a5deab;\n",
              "            color:  #000000;\n",
              "        }    #T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row12_col3 {\n",
              "            background-color:  #ccecd0;\n",
              "            color:  #000000;\n",
              "        }    #T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row12_col4 {\n",
              "            background-color:  #f7c8c0;\n",
              "            color:  #000000;\n",
              "        }    #T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row12_col5 {\n",
              "            background-color:  #6dca77;\n",
              "            color:  #000000;\n",
              "        }    #T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row12_col6 {\n",
              "            background-color:  #70cb79;\n",
              "            color:  #000000;\n",
              "        }    #T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row12_col7 {\n",
              "            background-color:  #f7c8c0;\n",
              "            color:  #000000;\n",
              "        }    #T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row12_col8 {\n",
              "            background-color:  #d0eed3;\n",
              "            color:  #000000;\n",
              "        }</style><table id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >F1-score</th>        <th class=\"col_heading level0 col1\" >VRand</th>        <th class=\"col_heading level0 col2\" >VInfo</th>        <th class=\"col_heading level0 col3\" >accuracy</th>        <th class=\"col_heading level0 col4\" >sensitivity</th>        <th class=\"col_heading level0 col5\" >specificity</th>        <th class=\"col_heading level0 col6\" >PPV</th>        <th class=\"col_heading level0 col7\" >NPV</th>        <th class=\"col_heading level0 col8\" >Jaccard</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >CDeep3M</th>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row0_col0\" class=\"data row0 col0\" >0.96607</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.96485</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row0_col2\" class=\"data row0 col2\" >0.96845</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.97450</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row0_col4\" class=\"data row0 col4\" >0.99303</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row0_col5\" class=\"data row0 col5\" >0.96383</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row0_col6\" class=\"data row0 col6\" >0.94054</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row0_col7\" class=\"data row0 col7\" >0.99585</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row0_col8\" class=\"data row0 col8\" >0.93437</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >EM-net V1 2x</th>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row1_col0\" class=\"data row1 col0\" >0.97390</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row1_col1\" class=\"data row1 col1\" >0.97961</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row1_col2\" class=\"data row1 col2\" >0.97815</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.98108</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row1_col4\" class=\"data row1 col4\" >0.96566</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row1_col5\" class=\"data row1 col5\" >0.98997</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row1_col6\" class=\"data row1 col6\" >0.98228</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row1_col7\" class=\"data row1 col7\" >0.98041</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row1_col8\" class=\"data row1 col8\" >0.94913</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >EM-net V1 4x</th>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row2_col0\" class=\"data row2 col0\" >0.97289</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.93384</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row2_col2\" class=\"data row2 col2\" >0.95655</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.98035</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row2_col4\" class=\"data row2 col4\" >0.96465</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.98940</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row2_col6\" class=\"data row2 col6\" >0.98128</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row2_col7\" class=\"data row2 col7\" >0.97983</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row2_col8\" class=\"data row2 col8\" >0.94722</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >EM-net V1 BN</th>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row3_col0\" class=\"data row3 col0\" >0.97468</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.95974</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.96392</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row3_col3\" class=\"data row3 col3\" >0.98153</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row3_col4\" class=\"data row3 col4\" >0.97254</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row3_col5\" class=\"data row3 col5\" >0.98671</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row3_col6\" class=\"data row3 col6\" >0.97683</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row3_col7\" class=\"data row3 col7\" >0.98422</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row3_col8\" class=\"data row3 col8\" >0.95061</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >EM-net V1 BN 2x</th>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row4_col0\" class=\"data row4 col0\" >0.97223</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.98270</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row4_col2\" class=\"data row4 col2\" >0.97845</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.97986</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row4_col4\" class=\"data row4 col4\" >0.96450</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row4_col5\" class=\"data row4 col5\" >0.98871</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row4_col6\" class=\"data row4 col6\" >0.98008</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row4_col7\" class=\"data row4 col7\" >0.97974</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row4_col8\" class=\"data row4 col8\" >0.94596</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >EM-net V2</th>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row5_col0\" class=\"data row5 col0\" >0.96445</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.96822</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row5_col2\" class=\"data row5 col2\" >0.96095</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row5_col3\" class=\"data row5 col3\" >0.97452</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row5_col4\" class=\"data row5 col4\" >0.94567</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row5_col5\" class=\"data row5 col5\" >0.99114</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.98399</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row5_col7\" class=\"data row5 col7\" >0.96939</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row5_col8\" class=\"data row5 col8\" >0.93135</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >EM-net V2 2x</th>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row6_col0\" class=\"data row6 col0\" >0.97219</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.97179</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row6_col2\" class=\"data row6 col2\" >0.96931</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row6_col3\" class=\"data row6 col3\" >0.97985</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row6_col4\" class=\"data row6 col4\" >0.96393</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row6_col5\" class=\"data row6 col5\" >0.98901</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.98060</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row6_col7\" class=\"data row6 col7\" >0.97942</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row6_col8\" class=\"data row6 col8\" >0.94589</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >EM-net V2 4x</th>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row7_col0\" class=\"data row7 col0\" >0.97353</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.97360</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.97557</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row7_col3\" class=\"data row7 col3\" >0.98079</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row7_col4\" class=\"data row7 col4\" >0.96659</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row7_col5\" class=\"data row7 col5\" >0.98897</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row7_col6\" class=\"data row7 col6\" >0.98057</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row7_col7\" class=\"data row7 col7\" >0.98091</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row7_col8\" class=\"data row7 col8\" >0.94842</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >PReLU-net</th>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row8_col0\" class=\"data row8 col0\" >0.94828</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row8_col1\" class=\"data row8 col1\" >0.88123</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row8_col2\" class=\"data row8 col2\" >0.92011</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row8_col3\" class=\"data row8 col3\" >0.96284</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row8_col4\" class=\"data row8 col4\" >0.93193</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row8_col5\" class=\"data row8 col5\" >0.98065</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row8_col6\" class=\"data row8 col6\" >0.96522</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row8_col7\" class=\"data row8 col7\" >0.96155</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row8_col8\" class=\"data row8 col8\" >0.90165</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >ResNet</th>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row9_col0\" class=\"data row9 col0\" >0.96320</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row9_col1\" class=\"data row9 col1\" >0.96219</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row9_col2\" class=\"data row9 col2\" >0.96586</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row9_col3\" class=\"data row9 col3\" >0.97286</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row9_col4\" class=\"data row9 col4\" >0.97174</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row9_col5\" class=\"data row9 col5\" >0.97350</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row9_col6\" class=\"data row9 col6\" >0.95481</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row9_col7\" class=\"data row9 col7\" >0.98355</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row9_col8\" class=\"data row9 col8\" >0.92901</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >SegNet</th>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row10_col0\" class=\"data row10 col0\" >0.96749</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row10_col1\" class=\"data row10 col1\" >0.95779</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row10_col2\" class=\"data row10 col2\" >0.95822</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row10_col3\" class=\"data row10 col3\" >0.97644</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row10_col4\" class=\"data row10 col4\" >0.95902</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row10_col5\" class=\"data row10 col5\" >0.98647</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row10_col6\" class=\"data row10 col6\" >0.97610</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row10_col7\" class=\"data row10 col7\" >0.97663</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row10_col8\" class=\"data row10 col8\" >0.93702</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >U-net</th>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row11_col0\" class=\"data row11 col0\" >0.96651</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row11_col1\" class=\"data row11 col1\" >0.96448</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row11_col2\" class=\"data row11 col2\" >0.95326</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row11_col3\" class=\"data row11 col3\" >0.97580</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row11_col4\" class=\"data row11 col4\" >0.95542</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row11_col5\" class=\"data row11 col5\" >0.98754</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row11_col6\" class=\"data row11 col6\" >0.97786</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row11_col7\" class=\"data row11 col7\" >0.97465</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row11_col8\" class=\"data row11 col8\" >0.93519</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002level0_row12\" class=\"row_heading level0 row12\" >VGG</th>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row12_col0\" class=\"data row12 col0\" >0.96454</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row12_col1\" class=\"data row12 col1\" >0.96459</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row12_col2\" class=\"data row12 col2\" >0.96355</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row12_col3\" class=\"data row12 col3\" >0.97449</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row12_col4\" class=\"data row12 col4\" >0.94938</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row12_col5\" class=\"data row12 col5\" >0.98895</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row12_col6\" class=\"data row12 col6\" >0.98020</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row12_col7\" class=\"data row12 col7\" >0.97136</td>\n",
              "                        <td id=\"T_7e959cf2_c3f7_11ea_b0a1_0242ac1c0002row12_col8\" class=\"data row12 col8\" >0.93151</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fad361d8198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ho0LRIQ3LqGU",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "outputId": "b7b6e841-5003-4f31-837a-5359e1ba91d6"
      },
      "source": [
        "#@markdown ###4.2.2 Play to visualise the heatmap report\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(6, 4), dpi=300)\n",
        "sns.set(font_scale=0.8, font=\"sans-serif\")\n",
        "csfont = {'fontname':'sans-serif'}\n",
        "heat_map = sns.heatmap(df, cmap=\"Spectral\")\n",
        "plt.suptitle(\"Heatmap of the segmentation methods relative to different metrics\", fontsize=16, y=1)\n",
        "plt.xlabel(\"Metrics\", fontsize=14, **csfont)\n",
        "plt.ylabel(\"Methods\", fontsize=14, **csfont)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x1200 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}