V2_Segmentation_of_Hippocampus.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# SEGMENTATION OF HIPPOCAMPUS"
      ],
      "metadata": {
        "id": "AsbcCDNcM_JH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Mounting Google Drive"
      ],
      "metadata": {
        "id": "euN_tpPFLsWf"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "moRtz1iatP-6",
        "outputId": "073159a6-8084-4560-d2aa-7bce8193acb0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jNefFntCuGBd",
        "outputId": "001c051b-fee0-45d5-f3e1-3be0f2c15279"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gdrive\tsample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd gdrive/MyDrive/Segmentation_of_Hippocampus/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UDjncIiDuvja",
        "outputId": "baaa55d1-93c2-4f7a-b98a-c1fa362cc3f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/gdrive/MyDrive/Segmentation_of_Hippocampus\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Importing Libraries"
      ],
      "metadata": {
        "id": "FfiJUPHiL9C6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install nibabel"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lo8Xoy6i5Me0",
        "outputId": "184146c1-e031-49fc-d0ba-b2dd98787f68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: nibabel in /usr/local/lib/python3.10/dist-packages (4.0.2)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from nibabel) (1.23.5)\n",
            "Requirement already satisfied: packaging>=17.0 in /usr/local/lib/python3.10/dist-packages (from nibabel) (23.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from nibabel) (67.7.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install split-folders"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7xkuktl7YLQG",
        "outputId": "5a9c441c-6798-4d03-c814-a7a2520eec69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting split-folders\n",
            "  Downloading split_folders-0.5.1-py3-none-any.whl (8.4 kB)\n",
            "Installing collected packages: split-folders\n",
            "Successfully installed split-folders-0.5.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from tifffile import imsave\n",
        "import nibabel as nib\n",
        "import glob\n",
        "import random\n",
        "import splitfolders\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from scipy.ndimage import zoom\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.utils import Sequence\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers, Model\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input, Conv3D, MaxPooling3D, concatenate, Conv3DTranspose, BatchNormalization, Dropout, Lambda\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.metrics import MeanIoU\n",
        "from keras.models import load_model"
      ],
      "metadata": {
        "id": "3sVBVCos2tiN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Exploring the Data"
      ],
      "metadata": {
        "id": "LRYJ76vSMdpP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()"
      ],
      "metadata": {
        "id": "VymCuYOC6xPz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "TRAIN_DATASET_PATH = '/content/gdrive/MyDrive/Segmentation_of_Hippocampus/Task04_Hippocampus/imagesTr'\n",
        "MASK_DATASET_PATH = '/content/gdrive/MyDrive/Segmentation_of_Hippocampus/Task04_Hippocampus/labelsTr'\n",
        "TEST_DATASET_PATH = '/content/gdrive/MyDrive/Segmentation_of_Hippocampus/Task04_Hippocampus/imagesTs'"
      ],
      "metadata": {
        "id": "UZL8fBCR5htM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_image=nib.load(TRAIN_DATASET_PATH + '/hippocampus_001.nii.gz').get_fdata()\n",
        "print(test_image.max())\n",
        "print(test_image.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1fo4cN335wGv",
        "outputId": "e4a67b10-a301-4175-ab23-33eb687c2b1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "139.0\n",
            "(35, 51, 35)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_image = scaler.fit_transform(test_image.reshape(-1, test_image.shape[-1])).reshape(test_image.shape)"
      ],
      "metadata": {
        "id": "VYxFdNR26sHs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(test_image.max())\n",
        "print(test_image.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-LS5uvIY7D_B",
        "outputId": "936e6e47-422b-417e-b916-26333a052a59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n",
            "(35, 51, 35)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# temp_img=test_image[:16, :16, :16]\n",
        "# temp_img = np.stack((temp_img,)*3, axis=-1)\n",
        "# #temp_img=np.expand_dims(temp_img, axis=-1)\n",
        "# print(temp_img.shape)"
      ],
      "metadata": {
        "id": "NU-wJ13jeAbw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def resize_volume(img, desired_depth, desired_width, desired_height):\n",
        "\n",
        "    current_depth = img.shape[0]\n",
        "    current_width = img.shape[1]\n",
        "    current_height = img.shape[2]\n",
        "\n",
        "    depth = current_depth / desired_depth\n",
        "    width = current_width / desired_width\n",
        "    height = current_height / desired_height\n",
        "\n",
        "    depth_factor = 1 / depth\n",
        "    width_factor = 1 / width\n",
        "    height_factor = 1 / height\n",
        "\n",
        "    img = zoom(img, (depth_factor, width_factor, height_factor), order=1)\n",
        "    return img"
      ],
      "metadata": {
        "id": "VPXT6SrV5GCf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "desired_depth = 64\n",
        "desired_width = 64\n",
        "desired_height = 64"
      ],
      "metadata": {
        "id": "8YomAIXhiSIF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_image = resize_volume(test_image, desired_depth, desired_width, desired_height)"
      ],
      "metadata": {
        "id": "fp4asG2Yibhw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(test_image.max())\n",
        "print(test_image.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YN4yONZYimMC",
        "outputId": "3965ec0b-2c7c-491a-b850-dcb4746f5eb1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n",
            "(64, 64, 64)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_mask=nib.load(MASK_DATASET_PATH + '/hippocampus_001.nii.gz').get_fdata()\n",
        "test_mask=test_mask.astype(np.uint8)"
      ],
      "metadata": {
        "id": "xxIqktuD7UJC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.unique(test_mask))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0DImR_v27UGg",
        "outputId": "2b2b4e69-79a9-43f9-f35b-21dc11c7ea4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(test_mask.max())\n",
        "print(test_mask.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vJsZOI9JfXlN",
        "outputId": "b22a81a2-1977-44d7-8df2-9adbd591003f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n",
            "(35, 51, 35)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# temp_mask=test_mask[:16, :16, :16]\n",
        "# temp_mask = np.expand_dims(temp_mask, axis=-1)\n",
        "# print(temp_mask.shape)"
      ],
      "metadata": {
        "id": "RM9S1bHqfdqn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_mask = resize_volume(test_mask, desired_depth, desired_width, desired_height)"
      ],
      "metadata": {
        "id": "jBrqzGdvjkrA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(test_mask.max())\n",
        "print(test_mask.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iJycLaaJjy7C",
        "outputId": "1550cf0a-33a7-400e-cdcf-08b699a4abba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n",
            "(64, 64, 64)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_slice=random.randint(0, test_mask.shape[2])\n",
        "\n",
        "plt.figure(figsize=(8, 4))\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "plt.imshow(test_image[:,:,n_slice], cmap='gray')\n",
        "plt.title('MRI')\n",
        "plt.subplot(1,2,2)\n",
        "plt.imshow(test_mask[:,:,n_slice])\n",
        "plt.title('Mask')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "C3uq0LhvMkZy",
        "outputId": "28a12f41-3831-4727-d95f-9e31393c777c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_img = np.stack((test_image,)*3, axis=-1)\n",
        "print(test_img.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vP3iFqIBkRuw",
        "outputId": "10529930-70f4-4c80-d9f4-fa637b194146"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(64, 64, 64, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_mask = np.expand_dims(test_mask, axis=-1)\n",
        "print(test_mask.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xw6spuGskFKa",
        "outputId": "4b8edfc7-bb41-45bc-aea0-baca7a73f98c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(64, 64, 64, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. Data Preprocessing"
      ],
      "metadata": {
        "id": "zGcAjtYpR-ig"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mri_list = sorted(glob.glob(TRAIN_DATASET_PATH + '/hippocampus_*.nii.gz'))\n",
        "mask_list = sorted(glob.glob(MASK_DATASET_PATH + '/hippocampus_*.nii.gz'))"
      ],
      "metadata": {
        "id": "zflug5wrQ9Xa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(mri_list))\n",
        "print(len(mask_list))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oBbf6_w4Rgms",
        "outputId": "8487d0f5-f188-42dc-8ca5-f1b67f083e5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "260\n",
            "260\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_mri_list = sorted(glob.glob(TEST_DATASET_PATH + '/hippocampus_*.nii.gz'))\n",
        "print(len(test_mri_list))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HXF_KmAylr3R",
        "outputId": "e5e46de1-2efa-4392-db69-057882b11682"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "130\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for img in range(len(mri_list)):\n",
        "  temp_img = nib.load(mri_list[img]).get_fdata()\n",
        "  temp_img = scaler.fit_transform(temp_img.reshape(-1, temp_img.shape[-1])).reshape(temp_img.shape)\n",
        "\n",
        "  temp_mask = nib.load(mask_list[img]).get_fdata()\n",
        "  temp_mask = temp_mask.astype(np.uint8)\n",
        "\n",
        "  temp_img=resize_volume(temp_img, desired_depth, desired_width, desired_height)\n",
        "  temp_img = np.stack((temp_img,)*3, axis=-1)\n",
        "  temp_mask=resize_volume(temp_mask, desired_depth, desired_width, desired_height)\n",
        "  temp_mask=np.expand_dims(temp_mask, axis=-1)\n",
        "\n",
        "  val, counts = np.unique(temp_mask, return_counts=True)\n",
        "\n",
        "  if (1 - (counts[0]/counts.sum())) > 0.01:  #At least 1% useful volume with labels that are not 0\n",
        "        # print(\"Save Me\")\n",
        "        temp_mask= to_categorical(temp_mask, num_classes=3)\n",
        "        np.save('v2_dataset/images/image_'+str(img)+'.npy', temp_img)\n",
        "        np.save('v2_dataset/masks/mask_'+str(img)+'.npy', temp_mask)\n",
        "\n",
        "  # else:\n",
        "  #     # print(\"I am useless\")"
      ],
      "metadata": {
        "id": "Su3bJuVJSDJP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting Dataset into training and validation\n",
        "input_folder = 'v2_dataset/'\n",
        "output_folder = 'v2_Data'\n",
        "\n",
        "splitfolders.ratio(input_folder, output=output_folder, seed=42, ratio=(.75, .25), group_prefix=None)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "80THo7skXOYT",
        "outputId": "8e03528f-fdd8-4c2c-eff9-1a91e6fe5a61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Copying files: 520 files [00:26, 19.42 files/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. Creating Custom Data Generator"
      ],
      "metadata": {
        "id": "FnLTFstQaqEY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def load_img(img_dir, img_list):\n",
        "    images=[]\n",
        "    for i, image_name in enumerate(img_list):\n",
        "        if (image_name.split('.')[1] == 'npy'):\n",
        "\n",
        "            image = np.load(img_dir+image_name)\n",
        "            # print(image.shape)\n",
        "\n",
        "            images.append(image)\n",
        "    images = np.array(images)\n",
        "\n",
        "    return(images)"
      ],
      "metadata": {
        "id": "D6b3NoECamoN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_img_dir = \"v2_Data/train/images/\"\n",
        "train_img_list = os.listdir(train_img_dir)"
      ],
      "metadata": {
        "id": "l01MKX9x6fac"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arr = load_img(train_img_dir, train_img_list[0:64])"
      ],
      "metadata": {
        "id": "d6zWW4yl6EmG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(arr[0].shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YO-KjVWeiJ-J",
        "outputId": "74528647-af6e-4fc4-b462-3294776e53aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(64, 64, 64, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def imageLoader(img_dir, img_list, mask_dir, mask_list, batch_size):\n",
        "    L = len(img_list)\n",
        "    while True:\n",
        "        batch_start = 0\n",
        "        batch_end = batch_size\n",
        "\n",
        "        while batch_start < L:\n",
        "            limit = min(batch_end, L)\n",
        "\n",
        "            X = load_img(img_dir, img_list[batch_start:limit])\n",
        "            Y = load_img(mask_dir, mask_list[batch_start:limit])\n",
        "\n",
        "            yield (X,Y) #a tuple with two numpy arrays with batch_size samples\n",
        "\n",
        "            batch_start += batch_size\n",
        "            batch_end += batch_size"
      ],
      "metadata": {
        "id": "AwTbTxw3ay-7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. Creating Model"
      ],
      "metadata": {
        "id": "1uHLwwvYMirx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kernel_initializer =  'he_uniform'"
      ],
      "metadata": {
        "id": "u8K3jZ04d-TH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def simple_unet_model(IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS, num_classes):\n",
        "#Build the model\n",
        "    inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS))\n",
        "    #s = Lambda(lambda x: x / 255)(inputs)   #No need for this if we normalize our inputs beforehand\n",
        "    s = inputs\n",
        "\n",
        "    #Contraction path\n",
        "    c1 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(s)\n",
        "    c1 = Dropout(0.1)(c1)\n",
        "    c1 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c1)\n",
        "    p1 = MaxPooling3D((2, 2, 2))(c1)\n",
        "\n",
        "    c2 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p1)\n",
        "    c2 = Dropout(0.1)(c2)\n",
        "    c2 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c2)\n",
        "    p2 = MaxPooling3D((2, 2, 2))(c2)\n",
        "\n",
        "    c3 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p2)\n",
        "    c3 = Dropout(0.2)(c3)\n",
        "    c3 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c3)\n",
        "    p3 = MaxPooling3D((2, 2, 2))(c3)\n",
        "\n",
        "    c4 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p3)\n",
        "    c4 = Dropout(0.2)(c4)\n",
        "    c4 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c4)\n",
        "    p4 = MaxPooling3D(pool_size=(2, 2, 2))(c4)\n",
        "\n",
        "    c5 = Conv3D(256, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p4)\n",
        "    c5 = Dropout(0.3)(c5)\n",
        "    c5 = Conv3D(256, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c5)\n",
        "\n",
        "    #Expansive path\n",
        "    u6 = Conv3DTranspose(128, (2, 2, 2), strides=(2, 2, 2), padding='same')(c5)\n",
        "    u6 = concatenate([u6, c4])\n",
        "    c6 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u6)\n",
        "    c6 = Dropout(0.2)(c6)\n",
        "    c6 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c6)\n",
        "\n",
        "    u7 = Conv3DTranspose(64, (2, 2, 2), strides=(2, 2, 2), padding='same')(c6)\n",
        "    u7 = concatenate([u7, c3])\n",
        "    c7 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u7)\n",
        "    c7 = Dropout(0.2)(c7)\n",
        "    c7 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c7)\n",
        "\n",
        "    u8 = Conv3DTranspose(32, (2, 2, 2), strides=(2, 2, 2), padding='same')(c7)\n",
        "    u8 = concatenate([u8, c2])\n",
        "    c8 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u8)\n",
        "    c8 = Dropout(0.1)(c8)\n",
        "    c8 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c8)\n",
        "\n",
        "    u9 = Conv3DTranspose(16, (2, 2, 2), strides=(2, 2, 2), padding='same')(c8)\n",
        "    u9 = concatenate([u9, c1])\n",
        "    c9 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u9)\n",
        "    c9 = Dropout(0.1)(c9)\n",
        "    c9 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c9)\n",
        "\n",
        "    outputs = Conv3D(num_classes, (1, 1, 1), activation='softmax')(c9)\n",
        "\n",
        "    model = Model(inputs=[inputs], outputs=[outputs])\n",
        "    #compile model outside of this function to make it flexible.\n",
        "    model.summary()\n",
        "\n",
        "    return model"
      ],
      "metadata": {
        "id": "L_GMA4r6dsIJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. Training the Model"
      ],
      "metadata": {
        "id": "ND0wwOsAMu7Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_img_dir = \"v2_Data/train/images/\"\n",
        "train_mask_dir = \"v2_Data/train/masks/\"\n",
        "\n",
        "train_img_list = os.listdir(train_img_dir)\n",
        "train_mask_list = os.listdir(train_mask_dir)"
      ],
      "metadata": {
        "id": "ZiDycF5Sf4_8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_img_dir = \"v2_Data/val/images/\"\n",
        "val_mask_dir = \"v2_Data/val/masks/\"\n",
        "\n",
        "val_img_list = os.listdir(val_img_dir)\n",
        "val_mask_list = os.listdir(val_mask_dir)"
      ],
      "metadata": {
        "id": "i-y49ROCgYLY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 8"
      ],
      "metadata": {
        "id": "DZO2qp9agy_m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_img_datagen = imageLoader(train_img_dir, train_img_list,\n",
        "                                train_mask_dir, train_mask_list, batch_size)\n",
        "\n",
        "val_img_datagen = imageLoader(val_img_dir, val_img_list,\n",
        "                                val_mask_dir, val_mask_list, batch_size)"
      ],
      "metadata": {
        "id": "Veh_ki0jgxwY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "metrics = ['accuracy']\n",
        "loss = tf.keras.losses.CategoricalCrossentropy()\n",
        "LR = 0.0001\n",
        "optim = tf.keras.optimizers.Adam(LR)"
      ],
      "metadata": {
        "id": "PLwBMM42haJs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "steps_per_epoch = len(train_img_list)//batch_size\n",
        "val_steps_per_epoch = len(val_img_list)//batch_size"
      ],
      "metadata": {
        "id": "YSBS0-iMjjHa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = simple_unet_model(IMG_HEIGHT=64,\n",
        "                          IMG_WIDTH=64,\n",
        "                          IMG_DEPTH=64,\n",
        "                          IMG_CHANNELS=3,\n",
        "                          num_classes=3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wjcho9Z6jlEH",
        "outputId": "28b28e56-b8c7-45be-9c15-505b9038ca95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)        [(None, 64, 64, 64, 3)]      0         []                            \n",
            "                                                                                                  \n",
            " conv3d (Conv3D)             (None, 64, 64, 64, 16)       1312      ['input_1[0][0]']             \n",
            "                                                                                                  \n",
            " dropout (Dropout)           (None, 64, 64, 64, 16)       0         ['conv3d[0][0]']              \n",
            "                                                                                                  \n",
            " conv3d_1 (Conv3D)           (None, 64, 64, 64, 16)       6928      ['dropout[0][0]']             \n",
            "                                                                                                  \n",
            " max_pooling3d (MaxPooling3  (None, 32, 32, 32, 16)       0         ['conv3d_1[0][0]']            \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3d_2 (Conv3D)           (None, 32, 32, 32, 32)       13856     ['max_pooling3d[0][0]']       \n",
            "                                                                                                  \n",
            " dropout_1 (Dropout)         (None, 32, 32, 32, 32)       0         ['conv3d_2[0][0]']            \n",
            "                                                                                                  \n",
            " conv3d_3 (Conv3D)           (None, 32, 32, 32, 32)       27680     ['dropout_1[0][0]']           \n",
            "                                                                                                  \n",
            " max_pooling3d_1 (MaxPoolin  (None, 16, 16, 16, 32)       0         ['conv3d_3[0][0]']            \n",
            " g3D)                                                                                             \n",
            "                                                                                                  \n",
            " conv3d_4 (Conv3D)           (None, 16, 16, 16, 64)       55360     ['max_pooling3d_1[0][0]']     \n",
            "                                                                                                  \n",
            " dropout_2 (Dropout)         (None, 16, 16, 16, 64)       0         ['conv3d_4[0][0]']            \n",
            "                                                                                                  \n",
            " conv3d_5 (Conv3D)           (None, 16, 16, 16, 64)       110656    ['dropout_2[0][0]']           \n",
            "                                                                                                  \n",
            " max_pooling3d_2 (MaxPoolin  (None, 8, 8, 8, 64)          0         ['conv3d_5[0][0]']            \n",
            " g3D)                                                                                             \n",
            "                                                                                                  \n",
            " conv3d_6 (Conv3D)           (None, 8, 8, 8, 128)         221312    ['max_pooling3d_2[0][0]']     \n",
            "                                                                                                  \n",
            " dropout_3 (Dropout)         (None, 8, 8, 8, 128)         0         ['conv3d_6[0][0]']            \n",
            "                                                                                                  \n",
            " conv3d_7 (Conv3D)           (None, 8, 8, 8, 128)         442496    ['dropout_3[0][0]']           \n",
            "                                                                                                  \n",
            " max_pooling3d_3 (MaxPoolin  (None, 4, 4, 4, 128)         0         ['conv3d_7[0][0]']            \n",
            " g3D)                                                                                             \n",
            "                                                                                                  \n",
            " conv3d_8 (Conv3D)           (None, 4, 4, 4, 256)         884992    ['max_pooling3d_3[0][0]']     \n",
            "                                                                                                  \n",
            " dropout_4 (Dropout)         (None, 4, 4, 4, 256)         0         ['conv3d_8[0][0]']            \n",
            "                                                                                                  \n",
            " conv3d_9 (Conv3D)           (None, 4, 4, 4, 256)         1769728   ['dropout_4[0][0]']           \n",
            "                                                                                                  \n",
            " conv3d_transpose (Conv3DTr  (None, 8, 8, 8, 128)         262272    ['conv3d_9[0][0]']            \n",
            " anspose)                                                                                         \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)   (None, 8, 8, 8, 256)         0         ['conv3d_transpose[0][0]',    \n",
            "                                                                     'conv3d_7[0][0]']            \n",
            "                                                                                                  \n",
            " conv3d_10 (Conv3D)          (None, 8, 8, 8, 128)         884864    ['concatenate[0][0]']         \n",
            "                                                                                                  \n",
            " dropout_5 (Dropout)         (None, 8, 8, 8, 128)         0         ['conv3d_10[0][0]']           \n",
            "                                                                                                  \n",
            " conv3d_11 (Conv3D)          (None, 8, 8, 8, 128)         442496    ['dropout_5[0][0]']           \n",
            "                                                                                                  \n",
            " conv3d_transpose_1 (Conv3D  (None, 16, 16, 16, 64)       65600     ['conv3d_11[0][0]']           \n",
            " Transpose)                                                                                       \n",
            "                                                                                                  \n",
            " concatenate_1 (Concatenate  (None, 16, 16, 16, 128)      0         ['conv3d_transpose_1[0][0]',  \n",
            " )                                                                   'conv3d_5[0][0]']            \n",
            "                                                                                                  \n",
            " conv3d_12 (Conv3D)          (None, 16, 16, 16, 64)       221248    ['concatenate_1[0][0]']       \n",
            "                                                                                                  \n",
            " dropout_6 (Dropout)         (None, 16, 16, 16, 64)       0         ['conv3d_12[0][0]']           \n",
            "                                                                                                  \n",
            " conv3d_13 (Conv3D)          (None, 16, 16, 16, 64)       110656    ['dropout_6[0][0]']           \n",
            "                                                                                                  \n",
            " conv3d_transpose_2 (Conv3D  (None, 32, 32, 32, 32)       16416     ['conv3d_13[0][0]']           \n",
            " Transpose)                                                                                       \n",
            "                                                                                                  \n",
            " concatenate_2 (Concatenate  (None, 32, 32, 32, 64)       0         ['conv3d_transpose_2[0][0]',  \n",
            " )                                                                   'conv3d_3[0][0]']            \n",
            "                                                                                                  \n",
            " conv3d_14 (Conv3D)          (None, 32, 32, 32, 32)       55328     ['concatenate_2[0][0]']       \n",
            "                                                                                                  \n",
            " dropout_7 (Dropout)         (None, 32, 32, 32, 32)       0         ['conv3d_14[0][0]']           \n",
            "                                                                                                  \n",
            " conv3d_15 (Conv3D)          (None, 32, 32, 32, 32)       27680     ['dropout_7[0][0]']           \n",
            "                                                                                                  \n",
            " conv3d_transpose_3 (Conv3D  (None, 64, 64, 64, 16)       4112      ['conv3d_15[0][0]']           \n",
            " Transpose)                                                                                       \n",
            "                                                                                                  \n",
            " concatenate_3 (Concatenate  (None, 64, 64, 64, 32)       0         ['conv3d_transpose_3[0][0]',  \n",
            " )                                                                   'conv3d_1[0][0]']            \n",
            "                                                                                                  \n",
            " conv3d_16 (Conv3D)          (None, 64, 64, 64, 16)       13840     ['concatenate_3[0][0]']       \n",
            "                                                                                                  \n",
            " dropout_8 (Dropout)         (None, 64, 64, 64, 16)       0         ['conv3d_16[0][0]']           \n",
            "                                                                                                  \n",
            " conv3d_17 (Conv3D)          (None, 64, 64, 64, 16)       6928      ['dropout_8[0][0]']           \n",
            "                                                                                                  \n",
            " conv3d_18 (Conv3D)          (None, 64, 64, 64, 3)        51        ['conv3d_17[0][0]']           \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 5645811 (21.54 MB)\n",
            "Trainable params: 5645811 (21.54 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer = optim, loss=loss, metrics=metrics)"
      ],
      "metadata": {
        "id": "auWC99k8jlAr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history=model.fit(train_img_datagen,\n",
        "          steps_per_epoch=steps_per_epoch,\n",
        "          epochs=100,\n",
        "          verbose=1,\n",
        "          validation_data=val_img_datagen,\n",
        "          validation_steps=val_steps_per_epoch,\n",
        "          )\n",
        "model.save('3d_unet_v2.hdf5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XI1bfCwJj4vq",
        "outputId": "e807ea73-6802-432a-f232-a1498a890a4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "24/24 [==============================] - 21s 877ms/step - loss: 0.5569 - accuracy: 0.8986 - val_loss: 0.3962 - val_accuracy: 0.9418\n",
            "Epoch 2/100\n",
            "24/24 [==============================] - 22s 934ms/step - loss: 0.2316 - accuracy: 0.9409 - val_loss: 0.2297 - val_accuracy: 0.9417\n",
            "Epoch 3/100\n",
            "24/24 [==============================] - 16s 658ms/step - loss: 0.1444 - accuracy: 0.9412 - val_loss: 0.2017 - val_accuracy: 0.9417\n",
            "Epoch 4/100\n",
            "24/24 [==============================] - 14s 601ms/step - loss: 0.1314 - accuracy: 0.9415 - val_loss: 0.1619 - val_accuracy: 0.9417\n",
            "Epoch 5/100\n",
            "24/24 [==============================] - 13s 568ms/step - loss: 0.1255 - accuracy: 0.9414 - val_loss: 0.1689 - val_accuracy: 0.9417\n",
            "Epoch 6/100\n",
            "24/24 [==============================] - 13s 539ms/step - loss: 0.1213 - accuracy: 0.9415 - val_loss: 0.1628 - val_accuracy: 0.9417\n",
            "Epoch 7/100\n",
            "24/24 [==============================] - 13s 550ms/step - loss: 0.1159 - accuracy: 0.9415 - val_loss: 0.1631 - val_accuracy: 0.9417\n",
            "Epoch 8/100\n",
            "24/24 [==============================] - 14s 592ms/step - loss: 0.1134 - accuracy: 0.9415 - val_loss: 0.1509 - val_accuracy: 0.9417\n",
            "Epoch 9/100\n",
            "24/24 [==============================] - 14s 573ms/step - loss: 0.1111 - accuracy: 0.9414 - val_loss: 0.1454 - val_accuracy: 0.9417\n",
            "Epoch 10/100\n",
            "24/24 [==============================] - 16s 688ms/step - loss: 0.1084 - accuracy: 0.9415 - val_loss: 0.1326 - val_accuracy: 0.9417\n",
            "Epoch 11/100\n",
            "24/24 [==============================] - 14s 582ms/step - loss: 0.1053 - accuracy: 0.9418 - val_loss: 0.1230 - val_accuracy: 0.9417\n",
            "Epoch 12/100\n",
            "24/24 [==============================] - 14s 584ms/step - loss: 0.1035 - accuracy: 0.9419 - val_loss: 0.1220 - val_accuracy: 0.9419\n",
            "Epoch 13/100\n",
            "24/24 [==============================] - 13s 552ms/step - loss: 0.1021 - accuracy: 0.9421 - val_loss: 0.1260 - val_accuracy: 0.9430\n",
            "Epoch 14/100\n",
            "24/24 [==============================] - 14s 588ms/step - loss: 0.1001 - accuracy: 0.9429 - val_loss: 0.1296 - val_accuracy: 0.9459\n",
            "Epoch 15/100\n",
            "24/24 [==============================] - 14s 591ms/step - loss: 0.0983 - accuracy: 0.9437 - val_loss: 0.1296 - val_accuracy: 0.9496\n",
            "Epoch 16/100\n",
            "24/24 [==============================] - 13s 551ms/step - loss: 0.0969 - accuracy: 0.9450 - val_loss: 0.1203 - val_accuracy: 0.9520\n",
            "Epoch 17/100\n",
            "24/24 [==============================] - 14s 588ms/step - loss: 0.0946 - accuracy: 0.9461 - val_loss: 0.1103 - val_accuracy: 0.9538\n",
            "Epoch 18/100\n",
            "24/24 [==============================] - 13s 542ms/step - loss: 0.0939 - accuracy: 0.9476 - val_loss: 0.1055 - val_accuracy: 0.9554\n",
            "Epoch 19/100\n",
            "24/24 [==============================] - 13s 559ms/step - loss: 0.0927 - accuracy: 0.9494 - val_loss: 0.0987 - val_accuracy: 0.9561\n",
            "Epoch 20/100\n",
            "24/24 [==============================] - 14s 590ms/step - loss: 0.0909 - accuracy: 0.9513 - val_loss: 0.0973 - val_accuracy: 0.9576\n",
            "Epoch 21/100\n",
            "24/24 [==============================] - 14s 592ms/step - loss: 0.0906 - accuracy: 0.9528 - val_loss: 0.0980 - val_accuracy: 0.9589\n",
            "Epoch 22/100\n",
            "24/24 [==============================] - 14s 594ms/step - loss: 0.0895 - accuracy: 0.9549 - val_loss: 0.1007 - val_accuracy: 0.9596\n",
            "Epoch 23/100\n",
            "24/24 [==============================] - 14s 593ms/step - loss: 0.0873 - accuracy: 0.9574 - val_loss: 0.1017 - val_accuracy: 0.9589\n",
            "Epoch 24/100\n",
            "24/24 [==============================] - 14s 594ms/step - loss: 0.0858 - accuracy: 0.9599 - val_loss: 0.1026 - val_accuracy: 0.9578\n",
            "Epoch 25/100\n",
            "24/24 [==============================] - 14s 595ms/step - loss: 0.0814 - accuracy: 0.9654 - val_loss: 0.1002 - val_accuracy: 0.9606\n",
            "Epoch 26/100\n",
            "24/24 [==============================] - 14s 594ms/step - loss: 0.0724 - accuracy: 0.9731 - val_loss: 0.0825 - val_accuracy: 0.9662\n",
            "Epoch 27/100\n",
            "24/24 [==============================] - 13s 546ms/step - loss: 0.0652 - accuracy: 0.9753 - val_loss: 0.0902 - val_accuracy: 0.9616\n",
            "Epoch 28/100\n",
            "24/24 [==============================] - 14s 594ms/step - loss: 0.0625 - accuracy: 0.9758 - val_loss: 0.0783 - val_accuracy: 0.9675\n",
            "Epoch 29/100\n",
            "24/24 [==============================] - 14s 608ms/step - loss: 0.0576 - accuracy: 0.9773 - val_loss: 0.0645 - val_accuracy: 0.9741\n",
            "Epoch 30/100\n",
            "24/24 [==============================] - 13s 563ms/step - loss: 0.0551 - accuracy: 0.9780 - val_loss: 0.0597 - val_accuracy: 0.9765\n",
            "Epoch 31/100\n",
            "24/24 [==============================] - 14s 597ms/step - loss: 0.0542 - accuracy: 0.9782 - val_loss: 0.0592 - val_accuracy: 0.9764\n",
            "Epoch 32/100\n",
            "24/24 [==============================] - 13s 560ms/step - loss: 0.0528 - accuracy: 0.9787 - val_loss: 0.0588 - val_accuracy: 0.9764\n",
            "Epoch 33/100\n",
            "24/24 [==============================] - 14s 596ms/step - loss: 0.0516 - accuracy: 0.9791 - val_loss: 0.0635 - val_accuracy: 0.9741\n",
            "Epoch 34/100\n",
            "24/24 [==============================] - 13s 544ms/step - loss: 0.0513 - accuracy: 0.9792 - val_loss: 0.0657 - val_accuracy: 0.9733\n",
            "Epoch 35/100\n",
            "24/24 [==============================] - 14s 596ms/step - loss: 0.0505 - accuracy: 0.9794 - val_loss: 0.0628 - val_accuracy: 0.9747\n",
            "Epoch 36/100\n",
            "24/24 [==============================] - 14s 600ms/step - loss: 0.0500 - accuracy: 0.9796 - val_loss: 0.0571 - val_accuracy: 0.9773\n",
            "Epoch 37/100\n",
            "24/24 [==============================] - 13s 548ms/step - loss: 0.0498 - accuracy: 0.9796 - val_loss: 0.0546 - val_accuracy: 0.9785\n",
            "Epoch 38/100\n",
            "24/24 [==============================] - 14s 600ms/step - loss: 0.0497 - accuracy: 0.9796 - val_loss: 0.0569 - val_accuracy: 0.9775\n",
            "Epoch 39/100\n",
            "24/24 [==============================] - 13s 550ms/step - loss: 0.0492 - accuracy: 0.9799 - val_loss: 0.0637 - val_accuracy: 0.9749\n",
            "Epoch 40/100\n",
            "24/24 [==============================] - 14s 596ms/step - loss: 0.0483 - accuracy: 0.9802 - val_loss: 0.0683 - val_accuracy: 0.9728\n",
            "Epoch 41/100\n",
            "24/24 [==============================] - 14s 594ms/step - loss: 0.0479 - accuracy: 0.9803 - val_loss: 0.0608 - val_accuracy: 0.9756\n",
            "Epoch 42/100\n",
            "24/24 [==============================] - 14s 599ms/step - loss: 0.0471 - accuracy: 0.9806 - val_loss: 0.0576 - val_accuracy: 0.9769\n",
            "Epoch 43/100\n",
            "24/24 [==============================] - 13s 558ms/step - loss: 0.0465 - accuracy: 0.9808 - val_loss: 0.0548 - val_accuracy: 0.9781\n",
            "Epoch 44/100\n",
            "24/24 [==============================] - 13s 555ms/step - loss: 0.0461 - accuracy: 0.9809 - val_loss: 0.0532 - val_accuracy: 0.9788\n",
            "Epoch 45/100\n",
            "24/24 [==============================] - 13s 560ms/step - loss: 0.0456 - accuracy: 0.9811 - val_loss: 0.0516 - val_accuracy: 0.9794\n",
            "Epoch 46/100\n",
            "24/24 [==============================] - 14s 601ms/step - loss: 0.0457 - accuracy: 0.9811 - val_loss: 0.0538 - val_accuracy: 0.9785\n",
            "Epoch 47/100\n",
            "24/24 [==============================] - 13s 552ms/step - loss: 0.0453 - accuracy: 0.9812 - val_loss: 0.0561 - val_accuracy: 0.9774\n",
            "Epoch 48/100\n",
            "24/24 [==============================] - 14s 599ms/step - loss: 0.0445 - accuracy: 0.9815 - val_loss: 0.0571 - val_accuracy: 0.9770\n",
            "Epoch 49/100\n",
            "24/24 [==============================] - 13s 552ms/step - loss: 0.0447 - accuracy: 0.9815 - val_loss: 0.0589 - val_accuracy: 0.9763\n",
            "Epoch 50/100\n",
            "24/24 [==============================] - 14s 602ms/step - loss: 0.0440 - accuracy: 0.9817 - val_loss: 0.0560 - val_accuracy: 0.9773\n",
            "Epoch 51/100\n",
            "24/24 [==============================] - 14s 598ms/step - loss: 0.0439 - accuracy: 0.9818 - val_loss: 0.0525 - val_accuracy: 0.9786\n",
            "Epoch 52/100\n",
            "24/24 [==============================] - 13s 557ms/step - loss: 0.0434 - accuracy: 0.9820 - val_loss: 0.0522 - val_accuracy: 0.9788\n",
            "Epoch 53/100\n",
            "24/24 [==============================] - 14s 597ms/step - loss: 0.0431 - accuracy: 0.9820 - val_loss: 0.0509 - val_accuracy: 0.9793\n",
            "Epoch 54/100\n",
            "24/24 [==============================] - 14s 567ms/step - loss: 0.0428 - accuracy: 0.9821 - val_loss: 0.0499 - val_accuracy: 0.9798\n",
            "Epoch 55/100\n",
            "24/24 [==============================] - 14s 612ms/step - loss: 0.0424 - accuracy: 0.9823 - val_loss: 0.0504 - val_accuracy: 0.9795\n",
            "Epoch 56/100\n",
            "24/24 [==============================] - 13s 554ms/step - loss: 0.0422 - accuracy: 0.9824 - val_loss: 0.0512 - val_accuracy: 0.9792\n",
            "Epoch 57/100\n",
            "24/24 [==============================] - 14s 599ms/step - loss: 0.0417 - accuracy: 0.9826 - val_loss: 0.0539 - val_accuracy: 0.9779\n",
            "Epoch 58/100\n",
            "24/24 [==============================] - 14s 597ms/step - loss: 0.0412 - accuracy: 0.9828 - val_loss: 0.0531 - val_accuracy: 0.9783\n",
            "Epoch 59/100\n",
            "24/24 [==============================] - 13s 549ms/step - loss: 0.0411 - accuracy: 0.9828 - val_loss: 0.0522 - val_accuracy: 0.9786\n",
            "Epoch 60/100\n",
            "24/24 [==============================] - 14s 601ms/step - loss: 0.0408 - accuracy: 0.9829 - val_loss: 0.0508 - val_accuracy: 0.9791\n",
            "Epoch 61/100\n",
            "24/24 [==============================] - 13s 559ms/step - loss: 0.0405 - accuracy: 0.9830 - val_loss: 0.0500 - val_accuracy: 0.9797\n",
            "Epoch 62/100\n",
            "24/24 [==============================] - 13s 550ms/step - loss: 0.0402 - accuracy: 0.9831 - val_loss: 0.0485 - val_accuracy: 0.9803\n",
            "Epoch 63/100\n",
            "24/24 [==============================] - 13s 555ms/step - loss: 0.0401 - accuracy: 0.9831 - val_loss: 0.0481 - val_accuracy: 0.9804\n",
            "Epoch 64/100\n",
            "24/24 [==============================] - 14s 598ms/step - loss: 0.0400 - accuracy: 0.9832 - val_loss: 0.0510 - val_accuracy: 0.9793\n",
            "Epoch 65/100\n",
            "24/24 [==============================] - 14s 596ms/step - loss: 0.0399 - accuracy: 0.9833 - val_loss: 0.0559 - val_accuracy: 0.9774\n",
            "Epoch 66/100\n",
            "24/24 [==============================] - 13s 561ms/step - loss: 0.0396 - accuracy: 0.9834 - val_loss: 0.0552 - val_accuracy: 0.9774\n",
            "Epoch 67/100\n",
            "24/24 [==============================] - 14s 594ms/step - loss: 0.0396 - accuracy: 0.9833 - val_loss: 0.0527 - val_accuracy: 0.9788\n",
            "Epoch 68/100\n",
            "24/24 [==============================] - 14s 597ms/step - loss: 0.0390 - accuracy: 0.9836 - val_loss: 0.0491 - val_accuracy: 0.9803\n",
            "Epoch 69/100\n",
            "24/24 [==============================] - 14s 600ms/step - loss: 0.0390 - accuracy: 0.9835 - val_loss: 0.0465 - val_accuracy: 0.9813\n",
            "Epoch 70/100\n",
            "24/24 [==============================] - 13s 557ms/step - loss: 0.0392 - accuracy: 0.9835 - val_loss: 0.0465 - val_accuracy: 0.9813\n",
            "Epoch 71/100\n",
            "24/24 [==============================] - 13s 558ms/step - loss: 0.0398 - accuracy: 0.9832 - val_loss: 0.0497 - val_accuracy: 0.9801\n",
            "Epoch 72/100\n",
            "24/24 [==============================] - 13s 548ms/step - loss: 0.0394 - accuracy: 0.9834 - val_loss: 0.0532 - val_accuracy: 0.9782\n",
            "Epoch 73/100\n",
            "24/24 [==============================] - 14s 597ms/step - loss: 0.0394 - accuracy: 0.9835 - val_loss: 0.0525 - val_accuracy: 0.9785\n",
            "Epoch 74/100\n",
            "24/24 [==============================] - 14s 598ms/step - loss: 0.0394 - accuracy: 0.9834 - val_loss: 0.0503 - val_accuracy: 0.9793\n",
            "Epoch 75/100\n",
            "24/24 [==============================] - 14s 598ms/step - loss: 0.0392 - accuracy: 0.9835 - val_loss: 0.0485 - val_accuracy: 0.9800\n",
            "Epoch 76/100\n",
            "24/24 [==============================] - 13s 555ms/step - loss: 0.0395 - accuracy: 0.9834 - val_loss: 0.0460 - val_accuracy: 0.9811\n",
            "Epoch 77/100\n",
            "24/24 [==============================] - 14s 600ms/step - loss: 0.0392 - accuracy: 0.9835 - val_loss: 0.0482 - val_accuracy: 0.9801\n",
            "Epoch 78/100\n",
            "24/24 [==============================] - 14s 598ms/step - loss: 0.0387 - accuracy: 0.9837 - val_loss: 0.0499 - val_accuracy: 0.9793\n",
            "Epoch 79/100\n",
            "24/24 [==============================] - 13s 565ms/step - loss: 0.0378 - accuracy: 0.9840 - val_loss: 0.0472 - val_accuracy: 0.9806\n",
            "Epoch 80/100\n",
            "24/24 [==============================] - 13s 575ms/step - loss: 0.0374 - accuracy: 0.9842 - val_loss: 0.0474 - val_accuracy: 0.9805\n",
            "Epoch 81/100\n",
            "24/24 [==============================] - 14s 599ms/step - loss: 0.0371 - accuracy: 0.9843 - val_loss: 0.0471 - val_accuracy: 0.9805\n",
            "Epoch 82/100\n",
            "24/24 [==============================] - 13s 553ms/step - loss: 0.0372 - accuracy: 0.9843 - val_loss: 0.0458 - val_accuracy: 0.9812\n",
            "Epoch 83/100\n",
            "24/24 [==============================] - 13s 554ms/step - loss: 0.0371 - accuracy: 0.9843 - val_loss: 0.0454 - val_accuracy: 0.9816\n",
            "Epoch 84/100\n",
            "24/24 [==============================] - 13s 562ms/step - loss: 0.0369 - accuracy: 0.9844 - val_loss: 0.0461 - val_accuracy: 0.9813\n",
            "Epoch 85/100\n",
            "24/24 [==============================] - 13s 562ms/step - loss: 0.0366 - accuracy: 0.9845 - val_loss: 0.0471 - val_accuracy: 0.9810\n",
            "Epoch 86/100\n",
            "24/24 [==============================] - 14s 599ms/step - loss: 0.0365 - accuracy: 0.9846 - val_loss: 0.0490 - val_accuracy: 0.9800\n",
            "Epoch 87/100\n",
            "24/24 [==============================] - 13s 562ms/step - loss: 0.0368 - accuracy: 0.9845 - val_loss: 0.0511 - val_accuracy: 0.9793\n",
            "Epoch 88/100\n",
            "24/24 [==============================] - 14s 600ms/step - loss: 0.0369 - accuracy: 0.9844 - val_loss: 0.0487 - val_accuracy: 0.9802\n",
            "Epoch 89/100\n",
            "24/24 [==============================] - 13s 554ms/step - loss: 0.0363 - accuracy: 0.9846 - val_loss: 0.0468 - val_accuracy: 0.9808\n",
            "Epoch 90/100\n",
            "24/24 [==============================] - 14s 574ms/step - loss: 0.0362 - accuracy: 0.9846 - val_loss: 0.0463 - val_accuracy: 0.9812\n",
            "Epoch 91/100\n",
            "24/24 [==============================] - 13s 559ms/step - loss: 0.0359 - accuracy: 0.9848 - val_loss: 0.0459 - val_accuracy: 0.9811\n",
            "Epoch 92/100\n",
            "24/24 [==============================] - 14s 599ms/step - loss: 0.0361 - accuracy: 0.9847 - val_loss: 0.0443 - val_accuracy: 0.9819\n",
            "Epoch 93/100\n",
            "24/24 [==============================] - 14s 605ms/step - loss: 0.0356 - accuracy: 0.9849 - val_loss: 0.0450 - val_accuracy: 0.9817\n",
            "Epoch 94/100\n",
            "24/24 [==============================] - 14s 573ms/step - loss: 0.0351 - accuracy: 0.9850 - val_loss: 0.0462 - val_accuracy: 0.9811\n",
            "Epoch 95/100\n",
            "24/24 [==============================] - 14s 608ms/step - loss: 0.0351 - accuracy: 0.9850 - val_loss: 0.0467 - val_accuracy: 0.9810\n",
            "Epoch 96/100\n",
            "24/24 [==============================] - 14s 603ms/step - loss: 0.0350 - accuracy: 0.9851 - val_loss: 0.0474 - val_accuracy: 0.9805\n",
            "Epoch 97/100\n",
            "24/24 [==============================] - 14s 574ms/step - loss: 0.0352 - accuracy: 0.9850 - val_loss: 0.0464 - val_accuracy: 0.9808\n",
            "Epoch 98/100\n",
            "24/24 [==============================] - 14s 599ms/step - loss: 0.0343 - accuracy: 0.9853 - val_loss: 0.0481 - val_accuracy: 0.9801\n",
            "Epoch 99/100\n",
            "24/24 [==============================] - 14s 599ms/step - loss: 0.0345 - accuracy: 0.9853 - val_loss: 0.0504 - val_accuracy: 0.9792\n",
            "Epoch 100/100\n",
            "24/24 [==============================] - 13s 559ms/step - loss: 0.0344 - accuracy: 0.9853 - val_loss: 0.0482 - val_accuracy: 0.9802\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6. Saving and Evaluating the Model"
      ],
      "metadata": {
        "id": "0aHma2KuM1Dn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Optionally, save the trained model\n",
        "# model.save(\"unet_model_v2.h5\")"
      ],
      "metadata": {
        "id": "hyxAj31z4r9z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "epochs = range(1, len(loss) + 1)\n",
        "plt.plot(epochs, loss, 'y', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "MMG4sfsujR4u",
        "outputId": "2e4327ba-448c-4168-d736-7f0999948918"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "plt.plot(epochs, acc, 'y', label='Training accuracy')\n",
        "plt.plot(epochs, val_acc, 'r', label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "2VT83ScBjUfL",
        "outputId": "76ae18a8-04e5-4d9d-a8ec-25cff4b633c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 7. Testing the Model"
      ],
      "metadata": {
        "id": "JxCB4EgzM6l6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for img in range(len(test_mri_list)):\n",
        "  temp_img = nib.load(test_mri_list[img]).get_fdata()\n",
        "\n",
        "  temp_img=resize_volume(temp_img, desired_depth, desired_width, desired_height)\n",
        "  temp_img = np.stack((temp_img,)*3, axis=-1)\n",
        "  np.save('v2_testing/image_'+str(img)+'.npy', temp_img)"
      ],
      "metadata": {
        "id": "N78mZpIew5Xv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "outputId": "353b7045-f351-483b-8974-d7a3abe3c56b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-85351f0f3c7c>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mimg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_mri_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m   \u001b[0mtemp_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_mri_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_fdata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0mtemp_img\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresize_volume\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp_img\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesired_depth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesired_width\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesired_height\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m   \u001b[0mtemp_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp_img\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'test_mri_list' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_model = load_model('3d_unet_v2.hdf5')"
      ],
      "metadata": {
        "id": "IGYxNoNFjhlD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(0,129):\n",
        "  img_path = \"v2_testing/image_\" + str(i) + \".npy\"\n",
        "  if os.path.exists(img_path):\n",
        "    test_img = np.load(img_path)\n",
        "\n",
        "    fig, axes = plt.subplots(nrows=1, ncols=6, figsize=(15,15))\n",
        "    slice_indices = [10, 20, 30 ,40 ,50 ,60]\n",
        "    for j, a in enumerate(axes.flatten()):\n",
        "      if j < len(slice_indices):\n",
        "        mri_slice = test_img[:, slice_indices[j], :, 1]\n",
        "        a.imshow(mri_slice)\n",
        "        a.axis(\"off\")\n",
        "    save_path = \"Images/image_\" + str(i) + \".png\"\n",
        "    plt.savefig(save_path, bbox_inches='tight')\n",
        "    plt.close()"
      ],
      "metadata": {
        "id": "EAuou6iXpw_W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(0,129):\n",
        "  img_path = \"v2_testing/image_\" + str(i) + \".npy\"\n",
        "  if os.path.exists(img_path):\n",
        "    test_img = np.load(img_path)\n",
        "    test_img_input = np.expand_dims(test_img, axis=0)\n",
        "    test_prediction = my_model.predict(test_img_input)\n",
        "    test_prediction_argmax=np.argmax(test_prediction, axis=4)[0,:,:,:]\n",
        "\n",
        "    fig, axes = plt.subplots(nrows=1, ncols=6, figsize=(15,15))\n",
        "    slice_indices = [10, 20, 30 ,40 ,50 ,60]\n",
        "    for j, a in enumerate(axes.flatten()):\n",
        "      if j < len(slice_indices):\n",
        "        mri_slice = test_prediction_argmax[:, slice_indices[j], :]\n",
        "        a.imshow(mri_slice)\n",
        "        a.axis(\"off\")\n",
        "    save_path = \"predictions/image_\" + str(i) + \".png\"\n",
        "    plt.savefig(save_path, bbox_inches='tight')\n",
        "    plt.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XHsAUVfDNQwt",
        "outputId": "89010160-301b-4428-8744-98296fe0b698"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 45ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
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            "1/1 [==============================] - 0s 29ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_img = np.load(\"v2_testing/image_13.npy\")"
      ],
      "metadata": {
        "id": "Q6YG2LBAyrt4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_img_input = np.expand_dims(test_img, axis=0)"
      ],
      "metadata": {
        "id": "NGKyTuhF0Tg8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_prediction = my_model.predict(test_img_input)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cwystRVf0eJJ",
        "outputId": "b0e8b80c-2e5e-41d2-895e-df0d1d3d9273"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 9s 9s/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_prediction_argmax=np.argmax(test_prediction, axis=4)[0,:,:,:]"
      ],
      "metadata": {
        "id": "UnK2y5zZ0eD0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_img.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-rJCL42AezIc",
        "outputId": "50843056-6c36-48c9-913c-8ec407c2eea6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(64, 64, 64, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_prediction.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ssF6gG6qKeew",
        "outputId": "b6ec9340-f0cb-4b86-e26a-45c70a126b89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 64, 64, 64, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_prediction_argmax.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VTHM5f_eKhT-",
        "outputId": "66fd2107-8411-4559-a7b2-f37f96170800"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(64, 64, 64)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(test_prediction_argmax)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fGrDmiKCMTJ0",
        "outputId": "509d331e-201b-4b8b-c03b-9ba7aa5811be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#n_slice=random.randint(0, test_prediction_argmax.shape[2])\n",
        "n_slice = random.randint(0,15)\n",
        "plt.figure(figsize=(12, 8))\n",
        "plt.subplot(231)\n",
        "plt.title('Testing Image')\n",
        "plt.imshow(test_img[:,:,n_slice,1], cmap='gray')\n",
        "plt.subplot(232)\n",
        "plt.title('Prediction on test image')\n",
        "plt.imshow(test_prediction_argmax[:,:, n_slice])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "qGToGuPK0eAj",
        "outputId": "63c71f94-15c7-444d-e317-1f4db37c9721"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize figure and axes\n",
        "fig, ax = plt.subplots()\n",
        "# Plot slice(s) of volume\n",
        "mri_slice = test_prediction_argmax[:,10,:]\n",
        "ax.imshow(mri_slice)\n",
        "# Edit axes and display figure\n",
        "ax.axis(\"off\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "_AubGcf6JuWM",
        "outputId": "b3c375a0-cc0a-422c-cee7-7643fbc79f20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize figure and axes\n",
        "fig, ax = plt.subplots()\n",
        "# Plot slice(s) of volume\n",
        "mri_slice = test_img[:,10,:,1]\n",
        "ax.imshow(mri_slice, cmap='gray')\n",
        "# Edit axes and display figure\n",
        "ax.axis(\"off\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "bTJmYMKBLAK1",
        "outputId": "8de0c289-3a10-4f14-d93e-ada7a63730b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(nrows=1, ncols=6, figsize=(15,15))\n",
        "slice_indices = [10, 20, 30 ,40 ,50 ,60]\n",
        "\n",
        "for i, a in enumerate(axes.flatten()):\n",
        "  if i < len(slice_indices):\n",
        "    mri_slice = test_prediction_argmax[:, slice_indices[i], :]\n",
        "    a.imshow(mri_slice)\n",
        "    a.axis(\"off\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "KlF4zJTWQWvM",
        "outputId": "0350f5c6-43ba-471c-8dc5-23bf1287ec6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 6 Axes>"
            ],
            "image/png": 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WrTvqd2jlNROa8lPafs1KDefrdHy4WcldbfJypwus2JRR4yMD8gdP1DAxAAAAUHvBlX06+CuO1q8Y1ac7n1banr9dcdudiH799ffpnss2a/+hDm3464S0/bl5Oz8ALEYUT8vY5khcmyNjksakpiOSpL/s7tYX/LfKnjn9v748YqthJ9NDAAAAgHxHVP/7q36s32k6/PyR+Zt6mrAj+lTTEX2q6Yh+p/Eqbf/etZq/WgsAFieKpyXGRIwirXk1pAq6vu240rZ1Xo9fExnTptVDmiokTh0brW/Q9FUtStfH5Y7Pyj8+yE4bALAEHKjk9LN8n3ZkVymaYZFaALhQdiKh8qs3aWZNRJnNRhuiw7WOBADLBsXTEmPigT688Um9v36H0pZRs504+4Ne4KZYTuvWfFsVnS6s7lu1UV8M3qLMhrRank0oNTrOXHMAWALuy23Ul565Vf54TCsHfHZIAoALZDc26Mj7HH3x1m+pw53WlkhZUqzWsQBgWaB4WmIs26jTm9Z678K2607YEa3+hXnqI7EBpVpyypXTKjbYSqdTZyyiaIJAplzmCw0ALDIzfkKVuYgic7acUqXWcQBg6XIdRZsK+qXk9PObRVA6AcB8oXiC1rhZfXz9ozrS26q7k1dKpk9e4XTJlBgtK7LjkILZ2RqmBAAAAAAASw3FE9TjpvTJhiPyGw7KswJ9L/cq2cXTU/HSh2PqOpCSKJ4AAAAAAMB5oHiCJJ0cUmykVbFJJbvmVCqdfmrki0mVV7cpEo3IZGYUZDI1TIqlbKqQ0P35HnV5GW30cmpzLmzKKAAAwHxwe7pVWtuuuY6IeptPPD/NDgAwnyiecIpj2Xp3ereuuHJAZeOcOv7NFTdqm71J8dGE2nY0yn5kll3vcEEmTtTrP82+S6lkUb+54cf6SN1ErSMBAIBL2MStK9T6b4/pxsZ+vb1upySv1pEAYNmheMIZetyUetxQUnjq2EjTc3q0e41yblSlo54SjiNjQhYbx7kxRgolK5ScrKMw52g65Wl4TaOk6hdPgQmf/ze/YGKehZIVWgqNJV+BZMQv5QCwlFiWCq2W/mDF3boh5ojSCQCqg+IJZ7UxOqzbNuzXka5mDZa61VnZoshMRe7+AQUTk7WOh0XOzGXVvLesxLirfKutfLd5Qa1ZXSVT0d25Zj2e7dPBuTZV8p6csz8MOCt3bEatu+IqNDr658QWSdKa+Ljek35OK9xUjdMBAAAAiwfFE87qqoitL3b/WFNBoF+1P6DDkRWKj3rqmWqSKJ5wFsFURpGfPquo6yr22stUaF+4t525sKy7Rq/Tjt1rZAWW7Ip19gcB58DvP6HkyJhS6bQss1Z3F65WojOrK7YMaIXLVGQAAADg5yiecFae5ajeistTWWvSk9rf2qFiEFGlJSWvpVmmVFaYzTL1Di/NGJlSSaZUkl1eqLFOp+X9iOyiLatiySlJdsWSW/ClcOGzYBkJA4XFQLZtyy0a2UVL5bKjinElUTwBwGJmRaNyOtpkknGVGo1ili8xJhoAqobiCecsarn6SPMj2nTdsB6bXqMd4UalN65X/eGyIo/vVZjP1zoi8LIiM5Zad/qKTRTljkzLL1dqHQkAANSAvapX+3+lVS2bx/VLnY9pJSNVAaCqKJ5wzhzL1g0x6YbYcV0dP6p/t7FLmXhaTimiZpenEhY3tyAl940rOHRUfq3DAACAmvGbk7r2Nfv196vvf/5IoqZ5AGC5oy3ABWmyi9rSMaR9Xpum801KX71WXqYg68QYC46j5o5Wsnqi2KsjpQ3ae6RLDQdtJcYCWblCraNhmTFBoMRoWXUHo8oVkvp674060bRHm6MndE305FTl+TYTFvRYsUH9lWZ9f+BKpQ55ik4aeVOFBVu4HwBwYfJhWX81s173jFyuA4c7tWGiIBarALDcUTzhgqzxPP0/Pf+suW5Xf9T0du101ys6EVPXQy4LjqPmflpYoy88+2YVJ+Pq+KmtpoeOyZRKCqZnah0Ny4wplxXZcUide+Oq9HXqaWuDtvWs0us3HNCG7nvV6Mz/r+jHfUufP/JWHT/RrPRzUfX+y7g0NSMzl533awEA5tdEWNaX73+z1n8jp02zUzInRiieACx7FE+4IFHL02rPkyRtrRvUk22rZSxPlYaoorGYTBDKVMo1TolLTT4sK1So46UWFSfiio65SoyW5A8Ns/g9qsMYBbOz0uys3HRSkUxCfjKi0WJaQZW+ShSNo8lcQnbGU3TKSCMTCjKZqlwLADC/isZSdNKRdu5T4DP5H8ClgeIJF+11qb3KbE3ocLZFu/21amnYqvh4WZGnDzPCBAtm2M/qa9PXatdst57cv1odP7MVmywrdnRCPqUTAAAAANQExRMu2g1R6br27epvLuij+Y9oJNqm9LGYug6lJYonLJDx0NU/9V+pycNNatpjqfEnhxWMT8o3rHoDAAAAALVi1zoAzo8JLO3Jd+mhorS3nFfF1H77V8ey5VmO0ralNfUTUldRuS6j8soWuatWyGlsrHVELBJutqz4mKXIqKvHptbooaK0u1xQPpyfaZlBaMkKJCuUFARSGDDFDjWRKcb1cLFd20oVTQS5eT13Powqm0koPmYrNh3KMFUDAM7OsuSuWaXwtVdp8vKEeuNMUQaAhcKIpyXGyjv6/p4tuid+ma7vOa4/7rpHnW6q1rEkSY12TJ/quE93tDTq26uu0zZtUmKkRy27inIfnuPLEeQcHFT3dJP85pT2B33695v/jTa1j+jzK7+v9Xak1vGA+WGkoYFm/e7su9WQKuh31t6r96Zm5+30xyotangqos77J2TNZOXn8vN2bgBYrux4XP3v6dLmd+/T9clx3dnwhKT53wACAPBiFE9LiZGsiiWNR1WyotqXbFO+s9ahTvMsR1ujjrZG88qHz+ix3tXKuTGlBzy5FoProJMLIGcychsbldq0UVN1SR3yWjTdS+mEZcAYWUaSkew5R5W5pEbroxpY2SRp/oqn6SChxGigYM+BeTsnACx7jqPcikBfXXH38zuOUjoBwEKheFoCTBAoMZhVy646VZKWct2WyvWsWwMAi8pcTk37KoqPuyq0Wsp3zN80z8CEerDo6eHsBv1wcLOSs7WfZg0AAACcC4qnpcAYmd2H1HA4KqujVUNv6VC5vtahAAAvFIxPKv5AVomIp9xNG1RsdTRf9ZCvQP8w9Rrds/MKRYddNY3NitXLAAAAsBQw/2mJMJWywrk5Wdm8rCX0Q7expCBqyW5qkFNXJ8ul68SZfN/RkN+ofj+rmbBQ6zjAhQsDhfm8gplZOeVQ89kMBcYo60dkFW05RUtWsIT+EGBJcHOWHix0aVupokzAumFAtXgyqqRD2WtWyu3ukhWN1joSAFQdLQCqyzaaWW2rnOxTdDZUw7Yh+ccHap0Ki0hhKq4/3Pc2NcSLel/XDn28vl+e5dQ6FgBcMsJyRb0/zutzE3cq12P08dt/rE83H6x1LGBZanVcvf/WR3V332bljrRq/TcbZHbuqXUsAKgqRjyhqowlFVtDZTYbZdY7ChsWxw58WDzsnKPpQ006urdTT8yuUSjWLwOABRUGsh7Zqbb/9qjW/GNWP5tcW+tEwLKVsmP64/Zd2nXdXXrPLU+o2JGsdSQAqDpGPC0xplxWajiQcRyNRJv1/3ddp03xE9oaHdJ6b/H84eryMlrbNa6hVJ1yM3FZ016tI2ERMb6v5IgvP+HJT0jFZqPAPTkvKTBGsmocEAAuVYbVw7C8uD3dym7tVqHZUcOqKXnstAwAC47iaYkJMjNK/+yQ6mIxpQd79A3zWrnNBX3i8kf0qcaDchbJH9OrI0X9ed+3NRXG9Ln+t2vPrhW1joRFJMzllXjskJK7Eiqta9eJ18YUsKsxAACYZ9mrupX81KDu7Nip62JHFbcitY4EAJcciqelJgwUTExKkuK9LYpMJFWyYzpRalTBlOXJUdSq/eiilB3TpoiUD8tqjWUZwYIzhYGCTEbKZOQ118n2Y7VOBABY5GwrVBCR7HRaqlQUlkqM0MJZldO27uzYqX9XPySJhbwBoBYonpYBy7f0wMA6/YdSSn3JcX2s4Qmt9lhLCQAALB+r3Kya3zSkAz2blT5iq/t7/fIHBmsdCwAAnMXimJeFi2L5luaO1+vR7Rv03UNbddyvq3UkAACAebXCTenuy/5e29/7p2p/V7+CjsZaRwIAAOeA4mkJcwoVRaek6IQtN2fJ8i0Fga2AeW1YQqxiRdGMUXTc0b5Mm7aVYtpdLigbFs/rPDErUEd6TmotqVxnSY5TpcTA2bk5X9FJS+6kp4y/eDZ+AM7GKvrad6JDf55ZpXvznvJhudaRzpCyY2p0Ekp6JRmHzzsAACwFTLVbwqxjJ9RdqihMxTR2bUqz7H6MJcgMjarjJ0ZhOqYT+Xb9avZO9TRN6zOrfqhb4uE5n6fLcfS7K/9FQ12N+j3/PbIS8SqmBl6BMXL3D6h3ukW51fV68lUrpdbdtU4FnBOrf0ir/nqtvtP2Fg3dEup7b/2KtrIsDgAAuAgUT0tYMD0jTc/ITiYVXXeFJMkYfv3D0hLOzUlzc7K8iBpWXKWRtpSOFF2N9NRLypzzeVJ2TDfHJMUy+pOWORmPtzfUTjAxKU1MKhmu00R+fkY8hcaWWEcZVRbMzsp58CmlJDW0vkZjb0pJqtQ4FQAAWMr4ZrYMGN9Xqr+owIsrm0np/217s55oPqzrk4d0c6wsz2LKEZa/TJDXj/LdOlRq19RggzpKw7WOBMgqljU+0K4/6t2o9bFhvTkxonr73EfjHa1k9aPcBvWXmvXInrVq3mUrlglkZ7I69/GAAAAAQO1QPC0DplSSu32fWp6LqmndCh3xVmlvV7f2Xd6ua3t/pHqLKUdY/oYCS39x7BYNHGtRw25XJn9+a0QB1WDyBdXvdvU167XqXTWhKzZ8S/WRc3/8U6UufWn3rSqOx9X+iK2mew/JFIsKCoXqhQYAAADmEYuLLxNhsahgekb2bEFeTrLyjmbLcYWGeRlYIkwopxzKKVgKC66KxjuvhweylC1FZOccOSUjGcaDYBEIjdyikZ1zlC1FVDmHP7uBCTUTFjTsZ3Wo1K7iZFzRcVexqUBhJqNwbk7G9xcgPC51Tsno6cIq7SiVNRHkah3nDCmvpGJLTG5Pt5w6dvPFy3PKRs/lurWjVNawn611HAC4JDHiCcCiYIJAyb3j6s02KrM+pmev6ZHqxmodC1hws2FRX566Vg9P9OnQ0XZ1PmgrOVxQpH9KfhDUOh4uIa1PTuvvv/pGfb31jXrVW57T/1jxoBxrcfxm+eHWx/TZX2/Rvok2tdy7Qo137ZCpLK4d+LA41O2a0KP//Vr9pOVV6nlDv7634R+VsM9j6CkA4KJRPAFYHIyRf+SY7CPH1FTeooFCY60TATUxZ0I9MLpeA3s6VH/EVuMj/fIHBsUYJyy08Jm9attlye3q1CPr+hSueECLZdXINyUquu2K72oyLOg1I7+lpu+6FE94ScGBw2o+eEROfZ32dW5Uab2vhCieAGAhUTwtM1a+qORQKMu39Vxrp77TtlYrvEltiUyq000teJ5QoY7NNSk54Cg2bmTlWHcHZ2cXfe2baNM/tNVplTehyyOWotb5Tb0DFoVKWYnxQJWkq0xDWvnwzOfxTFjQM+W4xv3TU4WOlPt0vL9FdcdsJUdCmWJpoVMDpxkjheGi3FHRseyTk1ctSRa7+uIVGCMTMAUfAGqF4mmZCcbG1fwTX83xmMYynfpc8W2qa8rpM5f9i96fmlnwPBUT6tixNm26e0LWTFbh5NSCZ8DS48zkVHmqS5/OvVc3rD6qP+39odociicsPcFsVqlHjiq9M64TYY/6b2rSDTr9Xny0YuuzR96p46PNpx8z56nzAVuNTwzKFIoKM5laRAcAAADmBcXTMmNKJfnDI5LtKLWuRbPDnmaDlAbLzSqZCdmy5VkLN1A+0MlFdc3AsIK5uQW7Lpa4iq/YlFQZjupoc5OmQ6nRDmTLetH6IoEJ5StQ0TiqBI6swJIV6OSv9ECthYGC8XFpXIpNdWncr9NMOCJPjhzL0mSY0FCmXmY0KstIMlIsays1mJd/rL/W6QFgWbF8S9NhKM8qKm5Far5mmWcFCj1Ldiwm4/tsHAFg2aJ4Wq5MqPixabXHm5RvcfWXqddqf1+7rkwN6kPp/Wp0ErVOCLwsMzen5ueKSo54msi367fj79W61Jje1fiUbo6dvl9gQt1XiOvuzFbtnu5QuK1BHQcDJYaKMgWmdWJxadif05f/4XZ9sTVQY9eMrmo7odFiWsW5qGxJsXFbdcdCeVlf3lCGNZ0AYB6Zclkdjxnd5vy23Pa8Pnf19/Xe1GxNM92c3q9vf/Aa2TdepdanjOru3qUwn69pJgCoBoqn5coYBQePKnGkX+mONvnxFbpv8ko9t7ZTb9y0T42LZXVQ4CUE0zNyH96llOMomrlceyOr9WxrjxpfldfNsX2n7ucr0N2Zrfrhji2Kjjla8WBO9lP7pCBQyK+GWGye3qs1e6KyEglNvblPD7yqUbKN7ODk2jSJYaOGHx9UODMn36/UOCwALC+mVFL6BztV/6OoSq9ap3/6o616b+qhmma6LZ7X4zf/V82FRrc2/abqH0hKFE8AliGKp+UsDGTCQKZcll0xssqWZgsx7Sx1qWhG1eUEanGSVbn0sJ/Vo8UuHSn3KTJlS2wBjvNkfF/yfdnlUHZFUtnW0XyLdpZOL7RcNK6GCnWyKpbsiiW77MuUWIgZi9PPp1FY5Ypik4Hio57Mz2d5GCk6G8gUiuzMhUXH+IE0HtXXZlaoNzKp10SnGDmNJcmUSgpKJTkFX+Ww9l+DPMtRi5NU2q7IioSSzSL5AJan2r/jYkFlx5L6bHi70vGSPrbqMX2ifqAq89u/Nn2t7rrrVtUdC7Vy34xCdmXCRbIqlh48tE5PjfacOhYaS3MzcVkBH9SwdBi/ouSuE4oPN8i8YCcuZ2pWPlNEsQiFs7Pq+05B39j+Dk1ttvSf7/i27kxP1joWAABYIiieLhHW8+ss2zlHxVxahWhKe9q6FNb3qxqz7nbO9Kj7wZysx55ZjDswYwmyfEsai2p2LPqCg6ef28CSYYz8E0PSiaEzDjM5FIuVKZVkPfaM6h6TIrdfpwO3d0gUTwAA4BxRPF0KCkXVHfdlBa7KdZYK7UZBlG/rWBq8yZwa90flx06PDKkkLRU6jMKIUXTSVmzCKDobypnK8uUdAC4BDxWlzx9/m/ozjWrYY7EbGM6LO5HVjoc36JqpFt3afVC/3/oI00cBoIooni4BwdycYo/sUzwaUfmKlRq8JaYgVetUwLkJDx9X0/C45JyeEhqs7dbAbSmVG43qjoVqeuCYTKGgIFeoYVIAwEL5xthNmvtKr1Y8Oy5NDStgfT+ch/DogNZ/pSiTjOsHH75Bd374cTbeAYAqoni6FBijcG5OmpPc2Q65hZj8oqWJUkqjQUFp21HKil70Wk+BCTUW5DUVOhrLp5XwQ6bZ4aL9fCHQF3Jam+QWUgrilrxcoGAqw6LiALAAbN/oaL5Zu8sFtTqh2qq0ScnZZMpxJYYKCg4eqcn1sbSZSln+4AnJspQYbdOO4kol7MNqd2zV2/FaxwOAZYfi6RLjnJhQ58Oeyg0RbQs36uNXJ9VXN6FPtt6vTZGLG2Lc7+f1rqc/IX97o1IDRunBo0x7QnWMTarjsYSChKvowLSCCs80AFgIiQPj2v31zbqj/XK13zSk7236O6YoYekyRm3b5vSl6Hv0hVajt7xhu77c9WStUwHAskPxdInxR0Zlj44pkUioLXWFDjk9OtrZrHc2Pq1NkYsbMTIUJKSfNWrFl7bJhEZ+GMxTauBMweSUrMczciUFhnF1ALBQ/CPH1PLXA7JjUR2ObdHcxlCNtQ4FXIwnn1Pndlvuyh7d17dRQecTVdnxGQAuZbyrXoqMkfF9xaZ8JQdthYMJ3Z3Zqn/NR7W3nFdgwgs+tRXq5AKflE6oNmNO/gMAWFhhIBMEC76raDYs6puzLfr1E9fr6YMr5cwxxRrzwJiTn1sLRfmHU/rk0Gv055lVGvazCxbBlq329mlNv36N/FuvkdvdtWDXBoCFwIinS5QplxV76qh6D6VVXNWsf9XVurdno96wdr8+1/mA6i3mtwMAgMVjf8XW//3DO7TyXyraMFWQOTZY60hYRoLJjNZ9c1L7792sn15/tZo+mtVH6iYW5Nqe5ejPNnxbP/79zXp4ok/TX+1V6jtDC3JtAFgIFE+XKmMUTExKE5OKSUoMdynnxHW4rUVz7YESViDPYnsPAADwEkIjK5DmQkf5sHzqsGNZilrevF4qMKFKxtdIUKfUMVvuT3bISGxggnllKmUFew7I3SM1Nl6vUb9e0sIUT5J0Q8zRDbF9+kG8X59t+qjYgBrAckLxBJnZObU8V1ZqyNXhoFf/MfYOrUlO6N31O3RNNFLreAAAYJExQaDWp0O9o+n/lImcnqK/tm9Ef7n2LvV58/e1+YtTG/RXO2+SNRbVyt1MrwMAYKmheIKCySlFfppV1HXlZTdrm71Bj7euVvs1s7omerzW8QAAwGITBkrd84w2PhiXbOvU4aEPbdS+32hRn1ecl8sEJtTX992gDZ/PS0PHFObzjHQCAGCJoXjCycXGSyWZclmR6bJi4wkVTUS7sj3am9yrtB2q3Ym/7NS7fj+rnaU2PZJdLy/Lx0EAAC4FYbEoFc8smNyCUcVc/MfLmbCgZ8pxDVWaVRpNyJock5/JXPR5gXPh5kP9bHKd1kTGtTEyqo1elJ3uAOAiUDzhNGMUOTSsnkKLSi1xPWCu1M5N3draekK/3/EjrX6ZYfP/ceCd2vvdjYqPhWp7elLsZwcAAC7Gj/Pt+vQPPqTGPZb6DhcVTs/UOhIuIcnnhjX1lZX64+bVCt+W0YPX/A823gGAi0DxhDP4I6PSyKjiLc1q6F2vKadJ2wNbM20vvVBoYEI9Pdijtd/tlz8wSOkEAAAu2t5il7oeDhX//jZJUniW+wPzyR8YVHJgUOlYTIf6rlLpap6BAHAxKJ5wVtlcTN/K3KBt8VFdFzuqKyKejvl5fX7kzdqbaZf7TEqmcKLWMQEAQI2lB3399pN36L+1T+pjPY/qg6nxs05Reqgo/fngGzVROD2yeqC/RetGWUgctWWCUPUHpLfu/N+0umFKv9l9r26MVXfKXbc7ralrfFkff7VSQ74Sjx1QwIg/AEscxRPOKpiM6rtPXSM7GugDl+/Q5randV9ug7Z/Y4s6fzqp+pnj8qemax0TAADUWPzR/Vp/tE2V1mb90W+8TXe85m/l6JW/qP/F8K0a/7M1Sh08/eV6U3FCZniMkU6oKVMpq+2fDkmPNmrksj79zW+/Tjeu+FlVr3l5xNL/fONf6NgtLfqDp96ltcfbJYonAEscxRNemjGyK5JTtqSyJeVshZ6rQ7lWTQQF9ZealR7yFezeX+ukAABgkQhmZ6XZWXmTzSqO9+lQxVfMeuWRS4czLWo5PKPwuX0LlBI4d8H4uDQ+rlR0sw5Mt+pwZ1Zp21KzHa/KguNRy9N1Uem66Iz+ujUjE0nP+zUAYKFRPOElmVxeTbtmlRxNnDoWRC09Fa7Xxyvv14Ghdq2eKNcwIQAAWKxMNqfee6U7xn5Lxnrl+9YdNbJGDy9MMOACOcMTKn97jd6+6ncUvXJa37nqb7TeS9Y6FgAsCRRPeElhsSjt3KPIC37JsWNRtUev1AF7haKTtrwMO9gBAIAXC4tFxX+wQyt/6Jz1viYIFIR8osDi5o+Mqvmbk2q2bI386rUauKJO6z2etwBwLiie8PKMkczpP6gmCGSFRgptydQwFwAAWPzCQIZCCcuI8X1JkhVIgbElfoIFgHNS3W0ZAAAAAAAAcMmieML5MZL1/D8AAADAJcdIFTmqmECBYe9FADgbptrh3AWBUseyCiJpReYCWVNs7QoAAIBLS9P+kj55z0flthb0Hy5/WL/ReKgqO9wBwHJB8YRzZnx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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(nrows=1, ncols=6, figsize=(15,15))\n",
        "slice_indices = [10, 20, 30 ,40 ,50 ,60]\n",
        "\n",
        "for i, a in enumerate(axes.flatten()):\n",
        "  if i < len(slice_indices):\n",
        "    mri_slice = test_img[:, slice_indices[i], :, 1]\n",
        "    a.imshow(mri_slice)\n",
        "    a.axis(\"off\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "aixWxQdeS8wB",
        "outputId": "2b677473-a675-4a24-b331-f08f66b407dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "\n",
        "# Create a figure\n",
        "fig = plt.figure()\n",
        "\n",
        "# Add a 3D subplot\n",
        "ax = fig.add_subplot(111, projection='3d')\n",
        "\n",
        "# Get the 3D coordinates of non-zero values in the array\n",
        "x, y, z = np.where(test_prediction_argmax > 0)\n",
        "\n",
        "# Plot the points\n",
        "ax.scatter(x, y, z)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "E686Q-EUTzIg",
        "outputId": "32faa2a4-2324-4986-9733-e839ca89ba99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "\n",
        "# Normalize the RGB values to be within 0-1 range\n",
        "test = test_img / np.max(test_img)\n",
        "\n",
        "\n",
        "# Create a figure\n",
        "fig = plt.figure()\n",
        "\n",
        "# Add a 3D subplot\n",
        "ax = fig.add_subplot(111, projection='3d')\n",
        "\n",
        "# Get the 3D coordinates of non-zero values in the array\n",
        "x, y, z = np.where(np.any(test > 0, axis=3))\n",
        "\n",
        "# Get the corresponding colors\n",
        "colors = test[x,y,z]\n",
        "\n",
        "# Plot the points with their corresponding colors\n",
        "ax.scatter(x, y, z, c=colors)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "xqJgVbwvVSFf",
        "outputId": "e8be39cb-c886-48c2-ffb4-81db176ecf50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}