Multi_ethnic metric.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/hua/Muscle_seg\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from monai.utils import  first, set_determinism\n",
    "import monai.transforms as transforms\n",
    "from monai.transforms import Compose\n",
    "from monai.visualize import matshow3d\n",
    "from torch import torch\n",
    "from monai.metrics import DiceMetric\n",
    "import glob as glob \n",
    "\n",
    "# Setup data directory\n",
    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
    "root_dir = os.getcwd() if directory is None else directory\n",
    "print(root_dir)\n",
    "\n",
    "# Set deterministic training for reproducibility\n",
    "set_determinism(seed=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dice for Algorithm selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_117.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_117_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_110.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_110_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_116.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_116_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_117.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_117_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_114.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_114_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_113.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_113_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_125.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_125_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_121.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_121_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_125.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_125_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_111.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_111_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_107.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_107_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_101.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_101_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_101.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_101_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_123.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_123_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_110.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_110_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_116.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_116_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_127.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_127_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_108.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_108_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_111.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_111_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_103.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_103_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_123.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_123_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_126.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_126_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_119.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_119_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_106.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_106_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_102.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_102_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_113.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_113_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_126.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_126_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_121.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_121_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_112.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_112_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_120.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_120_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_119.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_119_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_114.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_114_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_102.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_102_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_107.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_107_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_108.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_108_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_106.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_106_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_127.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_127_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_120.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_120_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/Helsinki_label/THIGH_103.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/Helsinki/THIGH_103_0000_seg.nii.gz'}]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def create_dict():\n",
    "    # Set the base path for the predictions\n",
    "    base_path = os.path.join(root_dir, './Data/Stage_1/dints_0/Helsinki/')   # MyoSegmenTUM, Helsinki\n",
    "    \n",
    "    # Set the path for the label folder\n",
    "    label_path = os.path.join(root_dir, './Data/Stage_1/Helsinki_label')\n",
    "    \n",
    "    # Find all predictions files\n",
    "    predict_files = glob.glob(os.path.join(base_path, 'THIGH_*.nii.gz'))\n",
    "    \n",
    "    # Initialize an empty list for the dictionary\n",
    "    ddict = []\n",
    "    \n",
    "    # Loop through each label file\n",
    "    for prediction in predict_files:\n",
    "        # Extract the prediction file name\n",
    "        stripped_base_name = os.path.basename(prediction).split('_')[0:2]\n",
    "        predict_name = \"_\".join(stripped_base_name) + '.nii.gz'\n",
    "        \n",
    "        # Extract the image file names for each type\n",
    "        label_files = glob.glob(os.path.join(label_path, predict_name))\n",
    "\n",
    "        # Create a dictionary for each set of files\n",
    "        for i in range(len(label_files)):\n",
    "            ddict.append({'label': label_files[i], 'predict': prediction})\n",
    "\n",
    "    return ddict\n",
    "\n",
    "Dice_dict = create_dict()\n",
    "Dice_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/P002_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/P002_1_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV001_2.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV001_2_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV008_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV008_1_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV008_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV008_1_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV001_3.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV001_3_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV005_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV005_1_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV003_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV003_1_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV001_2.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV001_2_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV007_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV007_1_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV009_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV009_1_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV004_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV004_1_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV005_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV005_1_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV002_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV002_1_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV003_2.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV003_2_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV003_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV003_1_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV006_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV006_1_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV002_3.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV002_3_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV003_3.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV003_3_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV002_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV002_1_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/P003_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/P003_1_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/P001_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/P001_1_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV002_2.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV002_2_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV006_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV006_1_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/P001_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/P001_1_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV007_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV007_1_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV001_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV001_1_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV009_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV009_1_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV002_3.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV002_3_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV002_2.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV002_2_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV003_2.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV003_2_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV003_3.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV003_3_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/P004_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/P004_1_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV001_3.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV001_3_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/P004_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/P004_1_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/P002_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/P002_1_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/P003_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/P003_1_0000_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV001_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV001_1_0001_seg.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_1/MyoSegmenTUM_label/HV004_1.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_1/dints_0/MyoSegmenTUM/HV004_1_0000_seg.nii.gz'}]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def create_dict():\n",
    "    # Set the base path for the predictions\n",
    "    base_path = os.path.join(root_dir, './Data/Stage_1/dints_0/MyoSegmenTUM/')   # MyoSegmenTUM, Helsinki\n",
    "    \n",
    "    # Set the path for the label folder\n",
    "    label_path = os.path.join(root_dir, './Data/Stage_1/MyoSegmenTUM_label')\n",
    "    \n",
    "    # Find all predictions files\n",
    "    predict_files = glob.glob(os.path.join(base_path, '*.nii.gz'))\n",
    "    \n",
    "    # Initialize an empty list for the dictionary\n",
    "    ddict = []\n",
    "    \n",
    "    # Loop through each label file\n",
    "    for prediction in predict_files:\n",
    "        # Extract the prediction file name\n",
    "        stripped_base_name = os.path.basename(prediction).split('_')[0:2]\n",
    "        predict_name = \"_\".join(stripped_base_name) + '.nii.gz'\n",
    "        \n",
    "        # Extract the image file names for each type\n",
    "        label_files = glob.glob(os.path.join(label_path, predict_name))\n",
    "\n",
    "        # Create a dictionary for each set of files\n",
    "        for i in range(len(label_files)):\n",
    "            ddict.append({'label': label_files[i], 'predict': prediction})\n",
    "\n",
    "    return ddict\n",
    "\n",
    "Dice_dict = create_dict()\n",
    "Dice_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([11, 320, 320, 21])\n",
      "torch.Size([11, 320, 320, 21])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "monai.transforms.io.dictionary LoadImaged.__init__:image_only: Current default value of argument `image_only=False` has been deprecated since version 1.1. It will be changed to `image_only=True` in version 1.3.\n"
     ]
    }
   ],
   "source": [
    "# label one-hot transfromation\n",
    "class ConvertToMultiChannelfromlabelsd(transforms.MapTransform):\n",
    "    \"\"\"\"\"\"\n",
    "    def __call__(self, data):\n",
    "        d = dict(data)\n",
    "        for key in self.keys:\n",
    "            #print(key)\n",
    "            # one-hot iteration\n",
    "            result = []\n",
    "            for i in range(1, 12):\n",
    "                result.append(d[key] == i)\n",
    "                #print(result[i-1].shape)\n",
    "                #print(len(result))\n",
    "            d[key] = torch.stack(result, axis=0)#.astype(np.float32)\n",
    "        return d\n",
    "\n",
    "\n",
    "Transform = Compose([\n",
    "    transforms.LoadImaged(keys=[\"predict\", \"label\"]), \n",
    "    ConvertToMultiChannelfromlabelsd(keys=[\"predict\", \"label\"]),\n",
    "    #transforms.EnsureChannelFirstd(keys=[\"predict\", \"label\"]), \n",
    "    transforms.EnsureTyped(keys=[\"predict\", \"label\"])\n",
    "    ])\n",
    "\n",
    "Test = Transform(Dice_dict[0])\n",
    "print(Test['predict'].shape)\n",
    "print(Test['label'].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.4294])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dice_metric = DiceMetric(include_background=True, reduction=\"mean\", get_not_nans=False)\n",
    "dice_metric(y_pred=Test['predict'], y=Test['label'])\n",
    "dice_metric.aggregate(reduction=\"mean\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P002_1_0000_seg.nii.gz\n",
      "HV001_2_0001_seg.nii.gz\n",
      "HV008_1_0001_seg.nii.gz\n",
      "HV008_1_0000_seg.nii.gz\n",
      "HV001_3_0000_seg.nii.gz\n",
      "HV005_1_0001_seg.nii.gz\n",
      "HV003_1_0000_seg.nii.gz\n",
      "HV001_2_0000_seg.nii.gz\n",
      "HV007_1_0000_seg.nii.gz\n",
      "HV009_1_0001_seg.nii.gz\n",
      "HV004_1_0001_seg.nii.gz\n",
      "HV005_1_0000_seg.nii.gz\n",
      "HV002_1_0001_seg.nii.gz\n",
      "HV003_2_0000_seg.nii.gz\n",
      "HV003_1_0001_seg.nii.gz\n",
      "HV006_1_0000_seg.nii.gz\n",
      "HV002_3_0001_seg.nii.gz\n",
      "HV003_3_0000_seg.nii.gz\n",
      "HV002_1_0000_seg.nii.gz\n",
      "P003_1_0001_seg.nii.gz\n",
      "P001_1_0000_seg.nii.gz\n",
      "HV002_2_0000_seg.nii.gz\n",
      "HV006_1_0001_seg.nii.gz\n",
      "P001_1_0001_seg.nii.gz\n",
      "HV007_1_0001_seg.nii.gz\n",
      "HV001_1_0000_seg.nii.gz\n",
      "HV009_1_0000_seg.nii.gz\n",
      "HV002_3_0000_seg.nii.gz\n",
      "HV002_2_0001_seg.nii.gz\n",
      "HV003_2_0001_seg.nii.gz\n",
      "HV003_3_0001_seg.nii.gz\n",
      "P004_1_0000_seg.nii.gz\n",
      "HV001_3_0001_seg.nii.gz\n",
      "P004_1_0001_seg.nii.gz\n",
      "P002_1_0001_seg.nii.gz\n",
      "P003_1_0000_seg.nii.gz\n",
      "HV001_1_0001_seg.nii.gz\n",
      "HV004_1_0000_seg.nii.gz\n",
      "mean=0.5511418463368165 \n",
      " std=0.09070322385043621\n"
     ]
    }
   ],
   "source": [
    "Dice_log = []\n",
    "\n",
    "for pair in Dice_dict:\n",
    "    MRI_pair = Transform(pair)\n",
    "    MRI_name = os.path.basename(MRI_pair['predict_meta_dict']['filename_or_obj'])\n",
    "    print(MRI_name)\n",
    "    dice_metric = DiceMetric(include_background=True, reduction=\"mean\", get_not_nans=False)\n",
    "    dice_metric(y_pred=MRI_pair['predict'], y=MRI_pair['label'])\n",
    "    dice_mean = dice_metric.aggregate(reduction=\"mean\").item()\n",
    "    Dice_log.append(dice_mean)\n",
    "\n",
    "\n",
    "mean = sum(Dice_log) / len(Dice_log)\n",
    "print(f'mean={mean} \\n std={np.std(Dice_log)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dice for the final model (testing MRIs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': '/home/hua/Muscle_seg/./Data/Stage_2/Label/THIGH_034.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_2/T2/THIGH_034_0003.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_2/Label/THIGH_061.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_2/T2/THIGH_061_0003.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_2/Label/THIGH_046.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_2/T2/THIGH_046_0003.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_2/Label/THIGH_064.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_2/T2/THIGH_064_0003.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_2/Label/THIGH_029.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_2/T2/THIGH_029_0003.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_2/Label/THIGH_038.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_2/T2/THIGH_038_0003.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_2/Label/THIGH_035.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_2/T2/THIGH_035_0003.nii.gz'},\n",
       " {'label': '/home/hua/Muscle_seg/./Data/Stage_2/Label/THIGH_054.nii.gz',\n",
       "  'predict': '/home/hua/Muscle_seg/./Data/Stage_2/T2/THIGH_054_0003.nii.gz'}]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def create_dict():\n",
    "    # Set the base path for the predictions\n",
    "    base_path = os.path.join(root_dir, './Data/Stage_2/T2/')  \n",
    "    \n",
    "    # Set the path for the label folder\n",
    "    label_path = os.path.join(root_dir, './Data/Stage_2/Label')\n",
    "    \n",
    "    # Find all predictions files\n",
    "    predict_files = glob.glob(os.path.join(base_path, '*.nii.gz'))\n",
    "    \n",
    "    # Initialize an empty list for the dictionary\n",
    "    ddict = []\n",
    "    \n",
    "    # Loop through each label file\n",
    "    for prediction in predict_files:\n",
    "        # Extract the prediction file name\n",
    "        stripped_base_name = os.path.basename(prediction).split('_')[0:2]\n",
    "        predict_name = \"_\".join(stripped_base_name) + '.nii.gz'\n",
    "        \n",
    "        # Extract the image file names for each type\n",
    "        label_files = glob.glob(os.path.join(label_path, predict_name))\n",
    "\n",
    "        # Create a dictionary for each set of files\n",
    "        for i in range(len(label_files)):\n",
    "            ddict.append({'label': label_files[i], 'predict': prediction})\n",
    "\n",
    "    return ddict\n",
    "\n",
    "Dice_dict = create_dict()\n",
    "Dice_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "monai.transforms.io.dictionary LoadImaged.__init__:image_only: Current default value of argument `image_only=False` has been deprecated since version 1.1. It will be changed to `image_only=True` in version 1.3.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([11, 560, 560, 65])\n",
      "torch.Size([11, 560, 560, 65])\n"
     ]
    }
   ],
   "source": [
    "# label one-hot transfromation\n",
    "class ConvertToMultiChannelfromlabelsd(transforms.MapTransform):\n",
    "    \"\"\"\"\"\"\n",
    "    def __call__(self, data):\n",
    "        d = dict(data)\n",
    "        for key in self.keys:\n",
    "            #print(key)\n",
    "            # one-hot iteration\n",
    "            result = []\n",
    "            for i in range(1, 12):\n",
    "                result.append(d[key] == i)\n",
    "                #print(result[i-1].shape)\n",
    "                #print(len(result))\n",
    "            d[key] = torch.stack(result, axis=0)#.astype(np.float32)\n",
    "        return d\n",
    "\n",
    "\n",
    "Transform = Compose([\n",
    "    transforms.LoadImaged(keys=[\"predict\", \"label\"]), \n",
    "    ConvertToMultiChannelfromlabelsd(keys=[\"predict\", \"label\"]),\n",
    "    #transforms.EnsureChannelFirstd(keys=[\"predict\", \"label\"]), \n",
    "    transforms.EnsureTyped(keys=[\"predict\", \"label\"])\n",
    "    ])\n",
    "\n",
    "Test = Transform(Dice_dict[0])\n",
    "print(Test['predict'].shape)\n",
    "print(Test['label'].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 5000x5000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 5000x5000 with 1 Axes>,\n",
       " array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan,  0.,  0., ...,  0.,  0., nan],\n",
       "        [nan,  0.,  0., ...,  0.,  0., nan],\n",
       "        ...,\n",
       "        [nan,  0.,  0., ..., nan, nan, nan],\n",
       "        [nan,  0.,  0., ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]], dtype=float32))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matshow3d(\n",
    "    volume=Test['label'], #val_label_convert, val_output_convert\n",
    "    fig=None,\n",
    "    title=\"input image\",\n",
    "    figsize=(50, 50),\n",
    "    every_n=1,\n",
    "    frame_dim=-1,\n",
    "    show=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.8146, 0.8004, 0.7683, 0.7624, 0.7341, 0.7716, 0.8225, 0.8486, 0.8683,\n",
       "        0.7729, 0.7366])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dice_metric = DiceMetric(include_background=True, reduction=\"mean\", get_not_nans=False)\n",
    "\n",
    "MRI_pair = Transform(Dice_dict[1])\n",
    "#MRI_name = os.path.basename(MRI_pair['predict_meta_dict']['filename_or_obj'])\n",
    "#print(MRI_name)\n",
    "dice_metric(y_pred=MRI_pair['predict'], y=MRI_pair['label'])\n",
    "dice_metrics = dice_metric.aggregate(reduction=\"mean_channel\")\n",
    "dice_metrics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "THIGH_034_0003.nii.gz\n",
      "THIGH_061_0003.nii.gz\n",
      "THIGH_046_0003.nii.gz\n",
      "THIGH_064_0003.nii.gz\n",
      "THIGH_029_0003.nii.gz\n",
      "THIGH_038_0003.nii.gz\n",
      "THIGH_035_0003.nii.gz\n",
      "THIGH_054_0003.nii.gz\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MRI_sample</th>\n",
       "      <th>SA</th>\n",
       "      <th>RF</th>\n",
       "      <th>VL</th>\n",
       "      <th>VI</th>\n",
       "      <th>VM</th>\n",
       "      <th>AM</th>\n",
       "      <th>GR</th>\n",
       "      <th>BL</th>\n",
       "      <th>ST</th>\n",
       "      <th>SM</th>\n",
       "      <th>BB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>THIGH_034_0003.nii.gz</td>\n",
       "      <td>0.796251</td>\n",
       "      <td>0.839367</td>\n",
       "      <td>0.798273</td>\n",
       "      <td>0.640001</td>\n",
       "      <td>0.757825</td>\n",
       "      <td>0.734285</td>\n",
       "      <td>0.830344</td>\n",
       "      <td>0.665251</td>\n",
       "      <td>0.540132</td>\n",
       "      <td>0.675827</td>\n",
       "      <td>0.650379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>THIGH_061_0003.nii.gz</td>\n",
       "      <td>0.885647</td>\n",
       "      <td>0.890167</td>\n",
       "      <td>0.885413</td>\n",
       "      <td>0.836773</td>\n",
       "      <td>0.794253</td>\n",
       "      <td>0.819574</td>\n",
       "      <td>0.875217</td>\n",
       "      <td>0.871137</td>\n",
       "      <td>0.811692</td>\n",
       "      <td>0.856184</td>\n",
       "      <td>0.697916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>THIGH_046_0003.nii.gz</td>\n",
       "      <td>0.843923</td>\n",
       "      <td>0.859978</td>\n",
       "      <td>0.829600</td>\n",
       "      <td>0.829457</td>\n",
       "      <td>0.776604</td>\n",
       "      <td>0.847166</td>\n",
       "      <td>0.846100</td>\n",
       "      <td>0.760266</td>\n",
       "      <td>0.892657</td>\n",
       "      <td>0.639665</td>\n",
       "      <td>0.794091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>THIGH_064_0003.nii.gz</td>\n",
       "      <td>0.869901</td>\n",
       "      <td>0.812183</td>\n",
       "      <td>0.808952</td>\n",
       "      <td>0.813167</td>\n",
       "      <td>0.772737</td>\n",
       "      <td>0.838556</td>\n",
       "      <td>0.873123</td>\n",
       "      <td>0.842747</td>\n",
       "      <td>0.778811</td>\n",
       "      <td>0.803098</td>\n",
       "      <td>0.844968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>THIGH_029_0003.nii.gz</td>\n",
       "      <td>0.815563</td>\n",
       "      <td>0.825572</td>\n",
       "      <td>0.777452</td>\n",
       "      <td>0.846487</td>\n",
       "      <td>0.796766</td>\n",
       "      <td>0.847243</td>\n",
       "      <td>0.792871</td>\n",
       "      <td>0.788052</td>\n",
       "      <td>0.822003</td>\n",
       "      <td>0.821169</td>\n",
       "      <td>0.746782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>THIGH_038_0003.nii.gz</td>\n",
       "      <td>0.867351</td>\n",
       "      <td>0.789369</td>\n",
       "      <td>0.752784</td>\n",
       "      <td>0.718887</td>\n",
       "      <td>0.725128</td>\n",
       "      <td>0.689019</td>\n",
       "      <td>0.888515</td>\n",
       "      <td>0.778683</td>\n",
       "      <td>0.778559</td>\n",
       "      <td>0.580527</td>\n",
       "      <td>0.785754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>THIGH_035_0003.nii.gz</td>\n",
       "      <td>0.829464</td>\n",
       "      <td>0.829281</td>\n",
       "      <td>0.831678</td>\n",
       "      <td>0.822591</td>\n",
       "      <td>0.834222</td>\n",
       "      <td>0.870360</td>\n",
       "      <td>0.765898</td>\n",
       "      <td>0.831538</td>\n",
       "      <td>0.847251</td>\n",
       "      <td>0.812851</td>\n",
       "      <td>0.680721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>THIGH_054_0003.nii.gz</td>\n",
       "      <td>0.816302</td>\n",
       "      <td>0.885722</td>\n",
       "      <td>0.889348</td>\n",
       "      <td>0.894853</td>\n",
       "      <td>0.897955</td>\n",
       "      <td>0.935646</td>\n",
       "      <td>0.824790</td>\n",
       "      <td>0.867185</td>\n",
       "      <td>0.800042</td>\n",
       "      <td>0.833008</td>\n",
       "      <td>0.823186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              MRI_sample        SA        RF        VL        VI        VM   \n",
       "0  THIGH_034_0003.nii.gz  0.796251  0.839367  0.798273  0.640001  0.757825  \\\n",
       "1  THIGH_061_0003.nii.gz  0.885647  0.890167  0.885413  0.836773  0.794253   \n",
       "2  THIGH_046_0003.nii.gz  0.843923  0.859978  0.829600  0.829457  0.776604   \n",
       "3  THIGH_064_0003.nii.gz  0.869901  0.812183  0.808952  0.813167  0.772737   \n",
       "4  THIGH_029_0003.nii.gz  0.815563  0.825572  0.777452  0.846487  0.796766   \n",
       "5  THIGH_038_0003.nii.gz  0.867351  0.789369  0.752784  0.718887  0.725128   \n",
       "6  THIGH_035_0003.nii.gz  0.829464  0.829281  0.831678  0.822591  0.834222   \n",
       "7  THIGH_054_0003.nii.gz  0.816302  0.885722  0.889348  0.894853  0.897955   \n",
       "\n",
       "         AM        GR        BL        ST        SM        BB  \n",
       "0  0.734285  0.830344  0.665251  0.540132  0.675827  0.650379  \n",
       "1  0.819574  0.875217  0.871137  0.811692  0.856184  0.697916  \n",
       "2  0.847166  0.846100  0.760266  0.892657  0.639665  0.794091  \n",
       "3  0.838556  0.873123  0.842747  0.778811  0.803098  0.844968  \n",
       "4  0.847243  0.792871  0.788052  0.822003  0.821169  0.746782  \n",
       "5  0.689019  0.888515  0.778683  0.778559  0.580527  0.785754  \n",
       "6  0.870360  0.765898  0.831538  0.847251  0.812851  0.680721  \n",
       "7  0.935646  0.824790  0.867185  0.800042  0.833008  0.823186  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MRI_sample = []; SA = []; RF = []; VL = []; VI = []; VM = []; AM = []; GR = []; BL = []; ST = []; SM = []; BB = []\n",
    "\n",
    "for pair in Dice_dict:\n",
    "    MRI_pair = Transform(pair)\n",
    "    MRI_name = os.path.basename(MRI_pair['predict_meta_dict']['filename_or_obj'])\n",
    "    print(MRI_name)\n",
    "    dice_metric = DiceMetric(include_background=True, reduction=\"mean\", get_not_nans=False)\n",
    "    dice_metric(y_pred=MRI_pair['predict'], y=MRI_pair['label'])\n",
    "    dice_metrics = dice_metric.aggregate(reduction=\"mean_channel\")\n",
    "    MRI_sample.append(MRI_name)\n",
    "    SA.append(dice_metrics[0].item())\n",
    "    RF.append(dice_metrics[1].item())\n",
    "    VL.append(dice_metrics[2].item())\n",
    "    VI.append(dice_metrics[3].item())\n",
    "    VM.append(dice_metrics[4].item())\n",
    "    AM.append(dice_metrics[5].item())\n",
    "    GR.append(dice_metrics[6].item())\n",
    "    BL.append(dice_metrics[7].item())\n",
    "    ST.append(dice_metrics[8].item())\n",
    "    SM.append(dice_metrics[9].item())\n",
    "    BB.append(dice_metrics[10].item())\n",
    "\n",
    "Dice_table = {'MRI_sample': MRI_sample, 'SA': SA, 'RF': RF, 'VL': VL, 'VI': VI, 'VM': VM, 'AM': AM, 'GR': GR, 'BL': BL, 'ST': ST, 'SM': SM, 'BB': BB}\n",
    "pd.DataFrame(Dice_table).to_excel('T2 final model.xlsx', index=False)\n",
    "pd.DataFrame(Dice_table)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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