Multi_ethnic Fat fraction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hua/miniconda3/envs/LGMD/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MONAI version: 1.1.0\n",
      "Numpy version: 1.23.1\n",
      "Pytorch version: 1.12.1\n",
      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
      "MONAI rev id: a2ec3752f54bfc3b40e7952234fbeb5452ed63e3\n",
      "MONAI __file__: /home/hua/miniconda3/envs/LGMD/lib/python3.8/site-packages/monai/__init__.py\n",
      "\n",
      "Optional dependencies:\n",
      "Pytorch Ignite version: 0.4.10\n",
      "Nibabel version: 4.0.2\n",
      "scikit-image version: 0.19.3\n",
      "Pillow version: 9.2.0\n",
      "Tensorboard version: 2.12.2\n",
      "gdown version: 4.7.1\n",
      "TorchVision version: 0.13.1\n",
      "tqdm version: 4.64.1\n",
      "lmdb version: 1.4.0\n",
      "psutil version: 5.9.0\n",
      "pandas version: 1.4.4\n",
      "einops version: 0.6.0\n",
      "transformers version: 4.21.3\n",
      "mlflow version: 2.2.2\n",
      "pynrrd version: 0.4.3\n",
      "\n",
      "For details about installing the optional dependencies, please visit:\n",
      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Requiremnts\n",
    "import os\n",
    "import shutil\n",
    "import tempfile\n",
    "import glob\n",
    "import re\n",
    "import matplotlib\n",
    "import numpy\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from monai.config import print_config\n",
    "from monai.data import DataLoader, Dataset, CacheDataset\n",
    "from monai.losses import DiceLoss\n",
    "from monai.metrics import DiceMetric\n",
    "from monai.networks.nets import UNet, BasicUNet\n",
    "from monai.utils import set_determinism\n",
    "from monai.transforms import (LoadImage, LoadImaged)\n",
    "\n",
    "os.environ[\"KMP_DUPLICATE_LIB_OK\"]  =  \"TRUE\"\n",
    "\n",
    "print_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/hua/Muscle_seg\n",
      "Rawdata_dicts example: \n",
      " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_001_0000.nii.gz', 'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_001_0001.nii.gz', 'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_001.nii.gz'} \n",
      "\n",
      "Rawdata_dicts length: \n",
      " 66 \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_001_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_001_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_001.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_002_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_002_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_002.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_003_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_003_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_003.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_004_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_004_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_004.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_005_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_005_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_005.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_006_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_006_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_006.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_007_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_007_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_007.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_008_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_008_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_008.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_009_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_009_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_009.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_010_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_010_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_010.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_011_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_011_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_011.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_012_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_012_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_012.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_013_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_013_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_013.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_014_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_014_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_014.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_015_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_015_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_015.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_016_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_016_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_016.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_017_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_017_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_017.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_018_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_018_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_018.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_019_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_019_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_019.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_020_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_020_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_020.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_021_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_021_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_021.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_022_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_022_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_022.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_023_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_023_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_023.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_024_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_024_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_024.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_025_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_025_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_025.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_026_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_026_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_026.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_027_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_027_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_027.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_028_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_028_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_028.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_029_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_029_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_029.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_030_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_030_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_030.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_031_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_031_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_031.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_032_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_032_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_032.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_033_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_033_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_033.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_034_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_034_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_034.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_035_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_035_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_035.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_036_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_036_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_036.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_037_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_037_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_037.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_038_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_038_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_038.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_039_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_039_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_039.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_040_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_040_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_040.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_041_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_041_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_041.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_042_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_042_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_042.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_043_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_043_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_043.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_044_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_044_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_044.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_045_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_045_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_045.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_046_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_046_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_046.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_047_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_047_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_047.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_048_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_048_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_048.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_049_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_049_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_049.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_050_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_050_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_050.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_051_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_051_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_051.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_052_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_052_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_052.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_053_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_053_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_053.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_054_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_054_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_054.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_055_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_055_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_055.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_056_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_056_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_056.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_057_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_057_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_057.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_058_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_058_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_058.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_059_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_059_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_059.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_060_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_060_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_060.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_061_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_061_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_061.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_062_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_062_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_062.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_063_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_063_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_063.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_064_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_064_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_064.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_065_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_065_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_065.nii.gz'},\n",
       " {'fat': '/home/hua/Muscle_seg/Data/Training/Fat/THIGH_066_0000.nii.gz',\n",
       "  'water': '/home/hua/Muscle_seg/Data/Training/Water/THIGH_066_0001.nii.gz',\n",
       "  'label': '/home/hua/Muscle_seg/Data/Training/Label/THIGH_066.nii.gz'}]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 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 tensorboard\n",
    "#writer = SummaryWriter()\n",
    "\n",
    "# Set deterministic training for reproducibility\n",
    "set_determinism(seed=0)\n",
    "\n",
    "# set Rawdata_dicts\n",
    "fat_series = sorted(\n",
    "    glob.glob(os.path.join(root_dir, \"Data\", \"Training\", \"Fat\",\"*.nii.gz\")))\n",
    "water_series = sorted(\n",
    "    glob.glob(os.path.join(root_dir, \"Data\", \"Training\", \"Water\",\"*.nii.gz\")))\n",
    "mask_series = sorted(\n",
    "    glob.glob(os.path.join(root_dir, \"Data\", \"Training\", \"Label\",\"*.nii.gz\")))\n",
    "\n",
    "Rawdata_dicts = [\n",
    "    {\"fat\": fat_name, \"water\": water_name, \"label\": label_name}\n",
    "    for fat_name, water_name, label_name,  in zip(fat_series, water_series, mask_series)\n",
    "]\n",
    "\n",
    "print(f\"Rawdata_dicts example: \\n {Rawdata_dicts[0]} \\n\")\n",
    "print(f\"Rawdata_dicts length: \\n {len(Rawdata_dicts)} \\n\")\n",
    "Rawdata_dicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hua\\AppData\\Local\\Temp\\ipykernel_23416\\2215604622.py:9: RuntimeWarning: invalid value encountered in true_divide\n",
      "  Fatfraction = numpy.divide(fat, Fatfraction_denominator)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x3456 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load function\n",
    "loadraw = LoadImaged(keys=[\"fat\", \"label\", \"water\"])\n",
    "# laod raw dict example\n",
    "Rawdata_dict_eg = loadraw(Rawdata_dicts[0])\n",
    "\n",
    "fat, water, label = Rawdata_dict_eg[\"fat\"], Rawdata_dict_eg[\"water\"], Rawdata_dict_eg[\"label\"]\n",
    "\n",
    "Fatfraction_denominator = numpy.add(fat, water)\n",
    "Fatfraction = numpy.divide(fat, Fatfraction_denominator)\n",
    "\n",
    "fig, ax = plt.subplots(1,4, figsize=(12, 48))\n",
    "\n",
    "cmap = matplotlib.colors.ListedColormap(['#000000', '#67001f', '#b2182b', '#d6604d', '#f4a582', '#fddbc7', '#f7f7f7', '#92c5de', '#4393c3', '#2166ac', '#053061', '#008b8b'])\n",
    "\n",
    "ax[0].imshow(fat[:, :, 5], cmap=\"gray\")\n",
    "ax[0].set(title='Fat (PD IDEAL)', xlabel= f\"shape: {fat.shape}\", ylabel='')\n",
    "ax[1].imshow(water[:, :, 5], cmap=\"gray\")\n",
    "ax[1].set(title='Water (PD IDEAL)', xlabel= f\"shape: {water.shape}\", ylabel='')\n",
    "ax[2].imshow(Fatfraction[:, :, 5], cmap=\"hot\")\n",
    "ax[2].set(title='Fat fraction (IDEAL)', xlabel= f\"shape: {Fatfraction.shape}\", ylabel='')\n",
    "ax[3].imshow(label[:, :, 5], interpolation='nearest', cmap=cmap)\n",
    "ax[3].set(title='label (Mask)', xlabel= f\"shape: {label.shape}\", ylabel='')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['fat', 'water', 'label', 'fat_meta_dict', 'label_meta_dict', 'water_meta_dict'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Rawdata_dict_eg.keys()\n",
    "#['Sartorius muscle', 'Rectus femoris muscle', 'Vastus lateralis muscle', 'Vastus intermedius muscle', 'Vastus medialis muscle', 'Adductor magnus muscle', 'Adductor longus muscle', 'Gracilis muscle', 'Biceps femoris muscle (long head)', 'Semitendinosus muscle', 'Semimembranosus muscle', 'Adductor brevis muscle', 'Biceps femoris muscle (short head)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('THIGH_001',\n",
       " [377.41594178771976,\n",
       "  311.7198143348694,\n",
       "  1053.499950618744,\n",
       "  471.4375100784302,\n",
       "  424.0418218574524,\n",
       "  203.71922984313966,\n",
       "  248.8054936103821,\n",
       "  222.47455570983888,\n",
       "  181.20234141921998,\n",
       "  206.9909144859314,\n",
       "  208.39056567001344],\n",
       " [93.482994213815,\n",
       "  74.7417626712314,\n",
       "  608.4421631211219,\n",
       "  294.38537329391596,\n",
       "  224.12052413099966,\n",
       "  151.20269532592448,\n",
       "  89.75799336177104,\n",
       "  162.0006347078734,\n",
       "  124.53801291160227,\n",
       "  150.0947828299514,\n",
       "  77.12745536849022],\n",
       " [0.24769222458121595,\n",
       "  0.23977225455080955,\n",
       "  0.5775436085818232,\n",
       "  0.6244419822362901,\n",
       "  0.5285340090967275,\n",
       "  0.7422112062879287,\n",
       "  0.36075567327435265,\n",
       "  0.728176011818456,\n",
       "  0.6872869960519872,\n",
       "  0.7251273960633328,\n",
       "  0.37011011088967233])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loadraw = LoadImaged(keys=[\"fat\", \"label\", \"water\"])\n",
    "\n",
    "def IDEAL_caculator(Rawdata_dict_one):\n",
    "    '''calculate each muscle voxel sum, fatfraction sum, and average fatfraction in each instance '''\n",
    "    import warnings\n",
    "    warnings.simplefilter('ignore')\n",
    "    Rawdata_dict_one = loadraw(Rawdata_dict_one)\n",
    "    address = Rawdata_dict_one['fat_meta_dict']['filename_or_obj']\n",
    "    patient_name = re.search(\"THIGH_...\", address).group(0)\n",
    "    voxel_size = numpy.prod(Rawdata_dict_one['fat_meta_dict']['pixdim'][1:4])\n",
    "    fat, water, label = Rawdata_dict_one[\"fat\"], Rawdata_dict_one[\"water\"], Rawdata_dict_one[\"label\"]\n",
    "    Fatfraction_denominator = numpy.add(fat, water)\n",
    "    Fatfraction = numpy.divide(fat, Fatfraction_denominator)\n",
    "    voxel_values = []\n",
    "    fatfraction_values = []\n",
    "    aver_fatfraction_values = []\n",
    "    for n in range(1, 12):\n",
    "        voxel_sum = (np.count_nonzero(label == n))*voxel_size*0.001\n",
    "        mask = label == n\n",
    "        fatfraction_sum = (sum(Fatfraction[mask]))*voxel_size*0.001\n",
    "        aver_fatfraction = fatfraction_sum/voxel_sum\n",
    "        voxel_values.append(voxel_sum)\n",
    "        fatfraction_values.append(fatfraction_sum)\n",
    "        aver_fatfraction_values.append(aver_fatfraction)\n",
    "    return patient_name, voxel_values, fatfraction_values, aver_fatfraction_values\n",
    "\n",
    "IDEAL_caculator(Rawdata_dicts[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.28368356649511034"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(IDEAL_caculator(Rawdata_dicts[0])[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[377.41594178771976, 311.8772750930786, 1065.0295772476197, 487.18358589935303, 427.0510719032288, 203.85919496154784, 249.01544128799438, 222.47455570983888, 181.20234141921998, 206.9909144859314, 208.60051334762574]\n",
      "[106.01811078929902, 135.98981014823914, 594.9388933181763, 368.7299052143097, 281.97459370422365, 385.57328632736204, 216.84861618232728, 241.47908636569977, 152.85963298988344, 205.34646424484254, 82.22055505657197]\n",
      "[374.86225637054446, 215.10475610923768, 1208.7045431728363, 950.7982138996124, 604.5008088130951, 712.6816135959625, 203.79307500076294, 238.5279341621399, 224.45117544937133, 187.36257254219055, 189.0079079761505]\n",
      "[404.95646339035034, 323.99425576400756, 1309.7924226264954, 818.9292928161622, 694.0089956474304, 544.472168334961, 295.2000545539856, 247.83108167266846, 299.8536628303528, 194.7244213142395, 306.7811933326721]\n",
      "[267.64577874660495, 173.55662056732177, 858.211576839447, 517.060708943367, 410.26680424022675, 329.3451044769287, 88.72170023059846, 184.95255345344543, 163.7339081144333, 158.26068744754792, 112.2935660495758]\n",
      "[231.2574398727417, 278.3629885959625, 1078.311188594818, 624.3369163360596, 584.2648090724945, 133.39741415023803, 177.7001628932953, 95.32164083480835, 96.55117101478577, 100.7289294757843, 139.00301411056518]\n",
      "[153.94495182418822, 185.92647350597383, 872.7307503919602, 634.561443025589, 512.4368600025177, 706.670031993866, 64.7981082906723, 260.4523556842804, 193.51530915927887, 222.4349261083603, 102.93273996162415]\n",
      "[148.90329700660706, 141.4072329559326, 666.1978193607331, 460.98810825920106, 270.2152612552643, 168.20533142280578, 56.597266668319705, 99.73599153137208, 89.68835717773437, 169.09110971450806, 72.55449649047851]\n",
      "[107.36498930740356, 166.43490338516236, 566.1483729972839, 320.49526792144775, 196.00952240753173, 161.21274222564696, 125.0938959274292, 155.34276866912842, 203.4130926589966, 179.0209728126526, 142.91534717559816]\n",
      "[260.25685636138917, 277.6170028362274, 1214.774230955124, 1191.2290276432038, 829.6212138156891, 850.3080488243103, 233.57875219726563, 384.4586112804413, 340.4042116680145, 520.4168188819885, 228.55642144966126]\n",
      "[230.68358911323548, 463.9019517021179, 1172.0062843875885, 1149.9038277931213, 905.6246354923248, 1177.6519162197114, 271.7968467750549, 489.0000496768952, 539.0810286502838, 366.58201250076297, 204.47172703552246]\n",
      "[344.0503734636307, 149.93948758602141, 1030.8819033193588, 685.1921244716644, 364.3314045858383, 242.83031200885773, 131.50939802646639, 131.21853578090668, 150.77241128921509, 121.34244044303894, 142.6282684135437]\n",
      "[305.5046951379776, 174.85246901988984, 1338.9289357910156, 907.4308402700425, 544.0424329328537, 756.605019695282, 132.3516719837189, 199.2600277452469, 267.73597581386565, 311.5406580400467, 127.9565533657074]\n",
      "[228.70467949295045, 278.7348374557495, 1055.736457927704, 822.1385770587922, 479.8405692539215, 490.8704588241577, 86.30121502685547, 257.9669926719666, 282.85287821769714, 278.7186882762909, 167.693079498291]\n",
      "[274.90667345237733, 329.27500378608704, 1297.664339627266, 589.9021131334305, 539.6729944610596, 337.8112326793671, 208.95141497325898, 197.3501642961502, 208.7938671245575, 173.50314901542663, 292.5233874435425]\n",
      "[166.91060638427734, 210.48584271240236, 897.9592313842774, 630.3866561279297, 629.2364602661133, 550.7983907470704, 125.01439160156251, 188.06363372802736, 217.33413531494142, 220.15013208007812, 144.5016180419922]\n",
      "[139.34575860977174, 127.14308923625946, 683.1908369941711, 436.5329794626236, 244.92595104408264, 333.4647124681473, 161.79443964576723, 187.41607913208009, 147.88630510520935, 153.161348528862, 158.5685985546112]\n",
      "[243.41556491470337, 157.05935863494872, 892.038986913681, 779.7838510408402, 354.1239040737152, 316.41908161354064, 159.61091218852997, 302.7755413684845, 295.7025820360184, 196.44318265628814, 176.4934711933136]\n",
      "[134.78458222389222, 145.69162296295167, 427.9393959789276, 213.80443979644775, 163.9493481273651, 125.27893096160889, 101.05869019317628, 91.38117041015626, 134.44084518241883, 232.3397987251282, 73.85058129501343]\n",
      "[224.06869507217408, 217.92095237636568, 691.0062790088654, 238.87616135025024, 259.11743891429904, 162.23430240917207, 151.11548174858095, 210.53044018936157, 209.72396211528778, 244.95779928588868, 131.1254022731781]\n",
      "[262.58865396881106, 288.2746215591431, 1207.681167366028, 878.9259645309448, 548.3450434112549, 454.89764661407474, 206.24321035766602, 176.77989459228516, 112.292167137146, 130.5703352508545, 85.82974464416505]\n",
      "[193.7388807964325, 134.45642266273498, 605.7149463415146, 514.8081260633469, 229.50138062953948, 449.3647345018387, 191.39878376483918, 129.77622859954835, 180.570877161026, 177.59617754459381, 150.09673220157623]\n",
      "[151.27141609954833, 206.2960301361084, 711.2876645317078, 660.8638591651917, 616.0059746322632, 726.8220041503906, 150.46495251083374, 246.36801599502564, 170.0448307876587, 366.3724421386719, 128.9416291923523]\n",
      "[220.7972427406311, 136.74066712570192, 802.4111041793824, 664.2822740402222, 381.797711101532, 396.7502098464966, 155.88409611129762, 156.0824051132202, 164.64935399627686, 96.74835173797608, 269.8985516166687]\n",
      "[162.47418295288085, 252.01831128692626, 868.2285763931275, 714.276267457962, 513.1886855945587, 715.2501959171295, 136.9515283317566, 249.18246077346802, 276.52407001686095, 192.4224830722809, 148.72460470581055]\n",
      "[181.47287658691405, 301.38532470703126, 1119.7786267089843, 732.6599304199219, 747.7790075683594, 843.2987585449218, 129.81602966308594, 315.7572381591797, 217.58578857421875, 360.0596502685547, 148.17281616210937]\n",
      "[208.8342416267395, 408.0570313882828, 1034.7845082378387, 481.1410835351944, 415.26231511116026, 289.9564818534851, 274.6336858263016, 109.77150596523285, 59.74436173152924, 77.12958759498596, 75.31835113620758]\n",
      "[244.72569464683534, 450.16049955749514, 1329.473618030548, 1033.107212600708, 842.2490054855347, 1066.113317960739, 194.22767193222046, 399.32553036117554, 416.8174477157593, 317.6819328594208, 113.78536188697815]\n",
      "[330.46678698349, 611.5646169319153, 1999.838422153473, 1339.276286201477, 723.9811396408081, 976.6558867034912, 275.34861545562745, 564.0044015350342, 547.7257267913818, 400.79776764678957, 131.82819636154176]\n",
      "[177.24976222229003, 282.7244100189209, 478.9987420806885, 216.05248110961915, 244.9000220336914, 132.76214755249023, 150.70262098693848, 134.1767611541748, 175.742603805542, 142.67766345214844, 127.61929997253418]\n",
      "[228.83462968826294, 98.05814195251465, 477.4197711696625, 297.9210478820801, 134.9268405818939, 384.2871452407837, 216.06058649253845, 139.30328268814088, 123.07330365943909, 131.03487546157837, 198.37717650604247]\n",
      "[154.57686235046387, 222.10838928222657, 879.8004155693054, 380.2019677963257, 472.36372861099244, 110.92198726654053, 205.26516976165772, 68.60240666580201, 85.80258609771728, 101.87900284576416, 213.72644173431397]\n",
      "[262.4454575805664, 196.9879213256836, 1146.605656890869, 659.9959732055664, 452.0349778747559, 621.2898830566406, 227.68029788208008, 210.2757425842285, 221.84947885131837, 176.55075411987306, 162.10555923461914]\n",
      "[447.3082889099121, 472.8587165222168, 1226.3179720687867, 715.1189636535645, 647.4923731384278, 235.91659165191652, 347.89016862487796, 169.96015523529053, 394.3175224685669, 292.74578242492674, 192.24352702331544]\n",
      "[131.225802570343, 120.13387748908997, 416.3768539142609, 434.68712356567386, 274.0194650287628, 322.78956231689455, 216.97339026641845, 89.8723560218811, 75.36956482505798, 189.38239188194277, 134.47802375030517]\n",
      "[430.7553503780365, 498.3971106777191, 1690.3172485370637, 1478.1213595046997, 1031.0050697250367, 1745.6395582580567, 354.1797726802826, 458.26583569908144, 506.99413734054565, 419.4108203487396, 130.62162779045104]\n",
      "[252.2096371564865, 264.27460483169557, 1408.9069552793503, 857.3380833644867, 638.3478173284531, 570.0438162813186, 181.00412241077424, 402.8959021453858, 284.0670732755661, 349.2475060043335, 105.47594439983368]\n",
      "[346.20945388412474, 198.86518521881104, 738.096292087555, 648.2354581604004, 298.3638811340332, 595.9213019371033, 343.4727770233154, 194.39660174560547, 344.2395753707886, 245.4812364807129, 302.42262411117554]\n",
      "[222.0990544128418, 321.3257625617981, 931.6000508613587, 516.2044975090027, 560.1408473892212, 403.13322429275513, 237.30610048675538, 539.0590176200867, 594.17357188797, 494.4194517364502, 180.60931021118165]\n",
      "[161.42456391334534, 192.61215984058381, 561.9584368305207, 499.2659485359192, 249.92382933807374, 463.31890600681305, 148.61372014331818, 72.50223656845093, 161.94017062854766, 82.74826744747162, 195.99665520191192]\n",
      "[146.46129460525512, 198.5649199924469, 905.6723293323518, 695.9886199588776, 694.6533075599671, 607.2630604629517, 87.49601441574097, 214.94563348007202, 176.97516605758668, 317.07720062446595, 149.21124489212036]\n",
      "[285.68814421081544, 303.3854532032013, 1061.290112804413, 889.6114502429963, 315.32428143882754, 509.0243868942261, 290.286491481781, 150.47985977554322, 267.04163509368897, 96.33326599311829, 200.07029295921325]\n",
      "[297.42197492122654, 284.0313200235367, 1278.4559281587601, 1598.806103205681, 953.417558836937, 1122.778577017784, 219.75910663604736, 305.2600497245789, 275.70977518558504, 378.82074801921846, 212.43380308151245]\n",
      "[327.7890463256836, 339.7296332244873, 1205.4536612091065, 936.1294217376709, 583.9135860443115, 446.94309274291993, 298.3467907562256, 149.0789619140625, 149.66654791259765, 251.71764472961425, 344.78706985473633]\n",
      "[138.87434270477294, 310.08747465705875, 904.9969185533524, 792.2606732902527, 678.3213087215423, 532.096039270401, 76.65588718605042, 212.14563052558898, 226.95325274848938, 307.6283516807556, 97.49232702827454]\n",
      "[259.9543725643158, 204.26969488334657, 907.9828260040283, 464.4488156719208, 500.18378332901, 167.25234029960632, 293.40219652748107, 131.21330151557922, 155.19525909233093, 109.83577924346925, 194.79060801315308]\n",
      "[457.67800801849364, 452.42206924057007, 1892.1151081447601, 1614.2816139621734, 1187.6593485488893, 1315.584328022003, 290.4020434341431, 528.5646257972718, 471.20633739471435, 535.5801614704133, 292.98430900764464]\n",
      "[338.754173915863, 462.3942252159119, 1361.2568693389892, 1018.9992080841065, 731.5545978012085, 882.2574362068176, 182.63084566497804, 316.51694484329226, 237.8272971382141, 412.168759552002, 183.87359211730958]\n"
     ]
    }
   ],
   "source": [
    "for a in Rawdata_dicts:\n",
    "    patient_name, voxel_values, fatfraction_values, aver_fatfraction_values = IDEAL_caculator(a)\n",
    "    print(voxel_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "patient = []; SA = []; RF = []; VL = []; VI = []; VM = []; AM = []; GR = []; BL = []; ST = []; SM = []; BB = []\n",
    "\n",
    "for a in Rawdata_dicts:\n",
    "    patient_name, voxel_values, fatfraction_values, aver_fatfraction_values = IDEAL_caculator(a)\n",
    "    patient.append(patient_name)\n",
    "    SA.append(voxel_values[0])\n",
    "    RF.append(voxel_values[1])\n",
    "    VL.append(voxel_values[2])\n",
    "    VI.append(voxel_values[3])\n",
    "    VM.append(voxel_values[4])\n",
    "    AM.append(voxel_values[5])\n",
    "    GR.append(voxel_values[6])\n",
    "    BL.append(voxel_values[7])\n",
    "    ST.append(voxel_values[8])\n",
    "    SM.append(voxel_values[9])\n",
    "    BB.append(voxel_values[10])\n",
    "\n",
    "voxel_values_table = {'patient': patient, '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(voxel_values_table).to_csv('muscle volumn.csv', encoding='utf_8_sig', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "patient = []; SA = []; RF = []; VL = []; VI = []; VM = []; AM = []; GR = []; BL = []; ST = []; SM = []; BB = []\n",
    "\n",
    "for a in Rawdata_dicts:\n",
    "    patient_name, voxel_values, fatfraction_values, aver_fatfraction_values = IDEAL_caculator(a)\n",
    "    patient.append(patient_name)\n",
    "    SA.append(aver_fatfraction_values[0])\n",
    "    RF.append(aver_fatfraction_values[1])\n",
    "    VL.append(aver_fatfraction_values[2])\n",
    "    VI.append(aver_fatfraction_values[3])\n",
    "    VM.append(aver_fatfraction_values[4])\n",
    "    AM.append(aver_fatfraction_values[5])\n",
    "    GR.append(aver_fatfraction_values[6])\n",
    "    BL.append(aver_fatfraction_values[7])\n",
    "    ST.append(aver_fatfraction_values[8])\n",
    "    SM.append(aver_fatfraction_values[9])\n",
    "    BB.append(aver_fatfraction_values[10])\n",
    "\n",
    "fatfraction_table = {'patient': patient, '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(fatfraction_table).to_excel('Huashan fat fraction.xlsx', encoding='utf_8_sig', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[21572, 17826, 60874, 27846, 24409, 11652, 14233, 12716, 10357, 11831, 11923]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary all raw NIFTI size\n",
    "loadraw = LoadImaged(keys=[\"fat\", \"label\", \"water\"])\n",
    "\n",
    "patient = []\n",
    "fat_shape = []\n",
    "water_shape = []\n",
    "mask_shape = []\n",
    "\n",
    "for a in Rawdata_dicts:\n",
    "    Rawdata_dict_a = loadraw(a)\n",
    "    fat, water, label = Rawdata_dict_a[\"fat\"], Rawdata_dict_a[\"water\"], Rawdata_dict_a[\"label\"]\n",
    "    address = Rawdata_dict_a['fat_meta_dict']['filename_or_obj']\n",
    "    patient_name = re.search(\"\\w+_\\w+(?= )\", address).group(0)\n",
    "    patient.append(patient_name)\n",
    "    fat_shape.append(fat.shape)\n",
    "    water_shape.append(water.shape)\n",
    "    mask_shape.append(label.shape)\n",
    "raw_data_size_table = {'patient': patient, 'Raw_fat_shape': fat_shape, 'Raw_water_shape': water_shape, 'Raw_mask_shape': mask_shape}\n",
    "pd.DataFrame(raw_data_size_table)\n",
    "    #print(f\"patient: {patient_name}, current fat shape:{fat.shape}, water shape:{water.shape}, mask shape:{label.shape}\") # for debug use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21572, 17826, 60874, 27846, 24409, 11652, 14233, 12716, 10357, 11831, 11923] [7969.078348100185, 6138.47610091418, 41936.12188017415, 20500.423490701243, 15699.68239713274, 9543.41626278311, 7173.199263879098, 10321.48460149765, 8186.696644503623, 9534.02485859394, 5786.629880335182] [0.3694176871917386, 0.3443552171499035, 0.6889003824321409, 0.7362071209761274, 0.6431923633550223, 0.8190367544441393, 0.5039836481331482, 0.8116927179535742, 0.7904505787876435, 0.8058511417964619, 0.48533337921120373]\n"
     ]
    }
   ],
   "source": [
    "voxel_values = []\n",
    "fatfraction_values = []\n",
    "aver_fatfraction_values = []\n",
    "for n in range(1, 12):\n",
    "    voxel_sum = np.count_nonzero(label == n)\n",
    "    mask = label == n\n",
    "    fatfraction_sum = sum(Fatfraction[mask])\n",
    "    aver_fatfraction = fatfraction_sum/voxel_sum\n",
    "    voxel_values.append(voxel_sum)\n",
    "    fatfraction_values.append(fatfraction_sum)\n",
    "    aver_fatfraction_values.append(aver_fatfraction)\n",
    "print (voxel_values, fatfraction_values, aver_fatfraction_values)"
   ]
  }
 ],
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  "interpreter": {
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  "kernelspec": {
   "display_name": "Python 3.8.12 ('IDEAL')",
   "language": "python",
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  "language_info": {
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    "name": "ipython",
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   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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