Tooth_Segmentation_3D_UNet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8cf71134",
   "metadata": {},
   "source": [
    "# Tooth Segmentation with 3D U-Net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e32a1f6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import nibabel as nib\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models\n",
    "from skimage import measure\n",
    "import vtk\n",
    "import h5py\n",
    "import pydicom\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf90d525",
   "metadata": {},
   "source": [
    "### Step 1: Load DICOM and Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f04d97bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_dicom_images(dicom_folder_path):\n",
    "    \"\"\"\n",
    "    Load DICOM images from a directory and return them as a 3D numpy array.\n",
    "    \"\"\"\n",
    "    # Collect all file paths in the folder with .dcm extension\n",
    "    dicom_files = [os.path.join(dicom_folder_path, f) for f in os.listdir(dicom_folder_path) if f.endswith('.dcm')]\n",
    "    \n",
    "    # Sort files to ensure consistent order\n",
    "    dicom_files = sorted(dicom_files)\n",
    "    \n",
    "    # Load each DICOM file, extract pixel array, and stack into a 3D volume\n",
    "    images = [pydicom.dcmread(file).pixel_array for file in dicom_files]\n",
    "    volume = np.stack(images, axis=-1)  # Stack along a new axis to create a 3D volume\n",
    "    volume = np.array(volume, dtype=np.float32)  # Ensure consistent data type\n",
    "    return volume"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d371535",
   "metadata": {},
   "source": [
    "### Step 2: Preprocessing - Normalizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "736a01cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(volume, min_bound=-1000, max_bound=400):\n",
    "    \"\"\"\n",
    "    Normalize the volume between 0 and 1.\n",
    "    \"\"\"\n",
    "    volume = (volume - min_bound) / (max_bound - min_bound)\n",
    "    volume = np.clip(volume, 0, 1)\n",
    "    return volume\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5e779292",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load and normalize DICOM data\n",
    "dicom_volume = load_dicom_images(r\"D:\\DIcom gans\\Data\\raw\")  # Using raw string to handle backslashes in path\n",
    "normalized_volume = normalize(dicom_volume)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f241882",
   "metadata": {},
   "source": [
    "### Step 3: Define 3D U-Net Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e5e5ce24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)            │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │ input_layer[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalization │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ conv3d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">27,680</span> │ batch_normalizat… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ conv3d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling3d       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalizat… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling3D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">55,360</span> │ max_pooling3d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ conv3d_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">110,656</span> │ batch_normalizat… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ conv3d_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling3d_1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalizat… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling3D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ max_pooling3d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">221,312</span> │ dropout[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)          │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ conv3d_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)          │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">442,496</span> │ batch_normalizat… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)          │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ conv3d_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)          │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_transpose    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">221,248</span> │ batch_normalizat… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3DTranspose</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_transpose… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)          │            │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">221,248</span> │ concatenate[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ conv3d_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">110,656</span> │ batch_normalizat… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ conv3d_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_transpose_1  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">55,328</span> │ batch_normalizat… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3DTranspose</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_1       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_transpose… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)           │            │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">55,328</span> │ concatenate_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ conv3d_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">27,680</span> │ batch_normalizat… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ conv3d_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span> │ batch_normalizat… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)            │            │                   │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
       "│ (\u001b[38;5;33mInputLayer\u001b[0m)        │ \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m1\u001b[0m)            │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d (\u001b[38;5;33mConv3D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │        \u001b[38;5;34m896\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│                     │ \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │        \u001b[38;5;34m128\u001b[0m │ conv3d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_1 (\u001b[38;5;33mConv3D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │     \u001b[38;5;34m27,680\u001b[0m │ batch_normalizat… │\n",
       "│                     │ \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │        \u001b[38;5;34m128\u001b[0m │ conv3d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling3d       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
       "│ (\u001b[38;5;33mMaxPooling3D\u001b[0m)      │ \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_2 (\u001b[38;5;33mConv3D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │     \u001b[38;5;34m55,360\u001b[0m │ max_pooling3d[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                     │ \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │        \u001b[38;5;34m256\u001b[0m │ conv3d_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_3 (\u001b[38;5;33mConv3D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │    \u001b[38;5;34m110,656\u001b[0m │ batch_normalizat… │\n",
       "│                     │ \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │        \u001b[38;5;34m256\u001b[0m │ conv3d_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling3d_1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n",
       "│ (\u001b[38;5;33mMaxPooling3D\u001b[0m)      │ \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ max_pooling3d_1[\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │ \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_4 (\u001b[38;5;33mConv3D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │    \u001b[38;5;34m221,312\u001b[0m │ dropout[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
       "│                     │ \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)          │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │        \u001b[38;5;34m512\u001b[0m │ conv3d_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)          │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_5 (\u001b[38;5;33mConv3D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │    \u001b[38;5;34m442,496\u001b[0m │ batch_normalizat… │\n",
       "│                     │ \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)          │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │        \u001b[38;5;34m512\u001b[0m │ conv3d_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)          │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_transpose    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │    \u001b[38;5;34m221,248\u001b[0m │ batch_normalizat… │\n",
       "│ (\u001b[38;5;33mConv3DTranspose\u001b[0m)   │ \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv3d_transpose… │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m128\u001b[0m)          │            │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_6 (\u001b[38;5;33mConv3D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │    \u001b[38;5;34m221,248\u001b[0m │ concatenate[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│                     │ \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │        \u001b[38;5;34m256\u001b[0m │ conv3d_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_7 (\u001b[38;5;33mConv3D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │    \u001b[38;5;34m110,656\u001b[0m │ batch_normalizat… │\n",
       "│                     │ \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │        \u001b[38;5;34m256\u001b[0m │ conv3d_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_transpose_1  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │     \u001b[38;5;34m55,328\u001b[0m │ batch_normalizat… │\n",
       "│ (\u001b[38;5;33mConv3DTranspose\u001b[0m)   │ \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_1       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ conv3d_transpose… │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m)           │            │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_8 (\u001b[38;5;33mConv3D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │     \u001b[38;5;34m55,328\u001b[0m │ concatenate_1[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                     │ \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │        \u001b[38;5;34m128\u001b[0m │ conv3d_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_9 (\u001b[38;5;33mConv3D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │     \u001b[38;5;34m27,680\u001b[0m │ batch_normalizat… │\n",
       "│                     │ \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │        \u001b[38;5;34m128\u001b[0m │ conv3d_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv3d_10 (\u001b[38;5;33mConv3D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │         \u001b[38;5;34m33\u001b[0m │ batch_normalizat… │\n",
       "│                     │ \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m1\u001b[0m)            │            │                   │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,552,481</span> (5.92 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,552,481\u001b[0m (5.92 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,551,201</span> (5.92 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,551,201\u001b[0m (5.92 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,280</span> (5.00 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m1,280\u001b[0m (5.00 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models\n",
    "\n",
    "def unet_model(input_shape=(128, 128, 64, 1)):\n",
    "    inputs = layers.Input(input_shape)\n",
    "    \n",
    "    # Encoder\n",
    "    c1 = layers.Conv3D(32, 3, activation=\"relu\", padding=\"same\")(inputs)\n",
    "    c1 = layers.BatchNormalization()(c1)\n",
    "    c1 = layers.Conv3D(32, 3, activation=\"relu\", padding=\"same\")(c1)\n",
    "    c1 = layers.BatchNormalization()(c1)\n",
    "    p1 = layers.MaxPooling3D((2, 2, 2))(c1)\n",
    "\n",
    "    c2 = layers.Conv3D(64, 3, activation=\"relu\", padding=\"same\")(p1)\n",
    "    c2 = layers.BatchNormalization()(c2)\n",
    "    c2 = layers.Conv3D(64, 3, activation=\"relu\", padding=\"same\")(c2)\n",
    "    c2 = layers.BatchNormalization()(c2)\n",
    "    p2 = layers.MaxPooling3D((2, 2, 2))(c2)\n",
    "    p2 = layers.Dropout(0.3)(p2)  # Dropout for regularization\n",
    "\n",
    "    c3 = layers.Conv3D(128, 3, activation=\"relu\", padding=\"same\")(p2)\n",
    "    c3 = layers.BatchNormalization()(c3)\n",
    "    c3 = layers.Conv3D(128, 3, activation=\"relu\", padding=\"same\")(c3)\n",
    "    c3 = layers.BatchNormalization()(c3)\n",
    "\n",
    "    # Decoder\n",
    "    u1 = layers.Conv3DTranspose(64, 3, strides=(2, 2, 2), padding=\"same\")(c3)\n",
    "    u1 = layers.concatenate([u1, c2])\n",
    "    c4 = layers.Conv3D(64, 3, activation=\"relu\", padding=\"same\")(u1)\n",
    "    c4 = layers.BatchNormalization()(c4)\n",
    "    c4 = layers.Conv3D(64, 3, activation=\"relu\", padding=\"same\")(c4)\n",
    "    c4 = layers.BatchNormalization()(c4)\n",
    "\n",
    "    u2 = layers.Conv3DTranspose(32, 3, strides=(2, 2, 2), padding=\"same\")(c4)\n",
    "    u2 = layers.concatenate([u2, c1])\n",
    "    c5 = layers.Conv3D(32, 3, activation=\"relu\", padding=\"same\")(u2)\n",
    "    c5 = layers.BatchNormalization()(c5)\n",
    "    c5 = layers.Conv3D(32, 3, activation=\"relu\", padding=\"same\")(c5)\n",
    "    c5 = layers.BatchNormalization()(c5)\n",
    "\n",
    "    # Output Layer\n",
    "    outputs = layers.Conv3D(1, 1, activation=\"sigmoid\")(c5)\n",
    "\n",
    "    model = models.Model(inputs, outputs)\n",
    "    return model\n",
    "\n",
    "# Instantiate the model\n",
    "model = unet_model()\n",
    "model.summary()\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b4fb29df",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'input_volumes' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[19], line 7\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m train_test_split\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m# Assuming `input_volumes` and `target_volumes` are numpy arrays containing your data\u001b[39;00m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;66;03m# Split data into training and validation sets\u001b[39;00m\n\u001b[1;32m----> 7\u001b[0m train_X, val_X, train_y, val_y \u001b[38;5;241m=\u001b[39m train_test_split(\u001b[43minput_volumes\u001b[49m, target_volumes, test_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'input_volumes' is not defined"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import time\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Assuming `input_volumes` and `target_volumes` are numpy arrays containing your data\n",
    "# Split data into training and validation sets\n",
    "train_X, val_X, train_y, val_y = train_test_split(input_volumes, target_volumes, test_size=0.2, random_state=42)"
   ]
  }
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