mr_preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **MR PREPROCESSING PIPELINE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-15 19:27:18.141721: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-08-15 19:27:18.895004: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import ants\n",
    "import numpy as np\n",
    "import nibabel as nib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pprint import pprint\n",
    "from pathlib import Path\n",
    "from concurrent.futures import ProcessPoolExecutor\n",
    "\n",
    "\n",
    "from preprocessing.utils import *\n",
    "from preprocessing.functions import *\n",
    "from preprocessing.pipeline import preprocess_brain_images\n",
    "from preprocessing.cropping import *\n",
    "from preprocessing.intensity_matching import *\n",
    "from intensity_normalization.plot.histogram import HistogramPlotter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !nvidia-smi\n",
    "# !nvcc --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "# !pip install antspynet\n",
    "# !pip install intensity-normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **0. TRANSFORM DICOM to NIFTI**\n",
    "Using dcm2niix library (not included in the Notebook).\n",
    "\n",
    "---------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DATA_DIR = '/mnt/c/Users/sergio/Desktop/Sergio/pydicom2niix/data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "GM_ROOT = '/mnt/c/Users/sergio/Desktop/TFG/GM'\n",
    "UCSF_ROOT = '/mnt/c/Users/sergio/Desktop/TFG/TCIA'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "GM_DIR_SpN = f'{GM_ROOT}/GM-SpN'  # Spatial Normalization\n",
    "GM_DIR_SkS = f'{GM_ROOT}/GM-SkS'  # Skull Stripped\n",
    "GM_DIR_BFC_DN = f'{GM_ROOT}/GM-BFC+DN'  # Bias Field Correction + Denoise\n",
    "GM_DIR_HM = f'{GM_ROOT}/GM-HM'  # Histogram matched\n",
    "GM_DIR_HM_NORM = f'{GM_ROOT}/GM-HM+NORM'  # Histogram matched + Normalized\n",
    "GM_DIR_NORM = f'{GM_ROOT}/GM-NORM'  # Normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "GM_BRATS = f'{GM_ROOT}/GM-BRATS'  # BraTS Pre-Processing Pipeline\n",
    "GM_BRATS_NORM = f'{GM_ROOT}/GM-BRATS+NORM'  # BraTS Pre-Processing Pipeline + Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "UCSF_BASE = f'{UCSF_ROOT}/UCSF-CLEANED'\n",
    "UCSF_NORM = f'{UCSF_ROOT}/UCSF-NORM'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_ucsf_mask(root, patient_no, mask_type='tumor'):\n",
    "    path = f'{root}/derivatives/sub-{patient_no:04}'\n",
    "    if mask_type == 'brain':\n",
    "        path += f'/sub-{patient_no:04}_desc-brain_mask.nii.gz'\n",
    "    elif mask_type == 'tumor':\n",
    "        path += f'/sub-{patient_no:04}_desc-tumor_dseg.nii.gz'\n",
    "\n",
    "    return ants.image_read(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **DATA STRUCTURE**\n",
    "1. **UCSF Data:** Public dataset from TCIA [The Cancer Imaging Archive](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=119705830)\n",
    "    - 501 patients\n",
    "    - T1w, T1w + contrast, T2w, FLAIR, DWI, SWI\n",
    "2. **GM Data:** Private dataset from Hospital Gregorio Marañón of Madrid.\n",
    "    - 41 patients\n",
    "    - T1w, T1w + contrast, T2w, FLAIR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gen_metadata(GM_DIR_SpN, f'{GM_ROOT}/GM_spatial_norm.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gen_metadata(GM_BRATS, f'{GM_ROOT}/GM_after_BraTS.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gen_metadata(GM_BRATS_NORM, f'{GM_ROOT}/GM_BraTS_with_normalization.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gen_metadata(GM_DIR_HM, f'{GM_ROOT}/GM_histogram_match.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gen_metadata(GM_DIR_HM_NORM, f'{GM_ROOT}/GM_histogram_match_normalization.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gen_metadata(UCSF_NORM, f'{UCSF_ROOT}/UCSF_normalized.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_series_info(scanner, json_path, property='Max', verbose=False):\n",
    "    json_ = load_json(json_path)\n",
    "    mins, means, vars_, maxs, sums = [], [], [], [], []\n",
    "    \n",
    "    for val in json_[scanner].values():\n",
    "        means.append(val['Mean'])\n",
    "        vars_.append(val['Var'])\n",
    "        mins.append(val['Min'])\n",
    "        maxs.append(val['Max'])\n",
    "        sums.append(val['Sum'])\n",
    "    \n",
    "    if verbose:\n",
    "        print(f'Mean MEAN: {np.mean(means):.2f}')\n",
    "        print(f'Mean VAR: {np.mean(vars_):.2f}')\n",
    "        print(f'Min MIN: {np.min(mins):.2f}')\n",
    "        print(f'Max MIN: {np.max(mins):.2f}')\n",
    "        print(f'Min MAX: {np.min(maxs):.2f}')\n",
    "        print(f'Mean MAX: {np.mean(maxs):.2f}')\n",
    "        print(f'Max MAX: {np.max(maxs):.2f}')\n",
    "        print(f'Min SUM: {np.min(sums):.2f}')\n",
    "        print(f'Mean SUM: {np.mean(sums):.2f}')\n",
    "        print(f'Max SUM: {np.max(sums):.2f}')\n",
    "        return\n",
    "    \n",
    "    return [np.min(mins), np.min(maxs), np.mean(maxs), np.max(maxs)]  # np.max(mins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **1. SPATIAL NORMALIZATION**\n",
    "1. **Resample the image:** Reshape the image to the same dimensions as the TCIA database.\n",
    " \n",
    "   1.1 **Interpolation method &rarr; Windowed Sinc Interpolation:** It is more appropriate as it tends to retain sharp edges better than other methods like B-Spline or Gaussian, which could be important in capturing the details of the tumor regions. This might be especially relevant in this case, as IDH status and 1p/19q codeletion are known to be associated with certain morphological characteristics on MRI.\n",
    "2. **Set spacing:** Resample spacing to 1 mm isotropic resolution.\n",
    "3. **Reorient:** Reorient the image to RAI (Right, Anterior, Inferior)\n",
    "4. **Set origin:** Set the origin of the image to the same as the TCIA database.\n",
    "5. **Set direction:** Set the direction of the image to the same as the TCIA database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UCSF DATA (Reference)\n",
    "- **Orientation:** RAI\n",
    "- **Dimensions:** (240, 240, 155)\n",
    "- **Spacing:** (1.0, 1.0, 1.0)\n",
    "- **Origin:** (-0.0, -239.0, 0.0)\n",
    "- **Direction:** [1. 0. 0. 0. 1. 0. 0. 0. 1.]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_padding_axis(image, target_size):\n",
    "    for axis in range(image.dimensions):  # Iterate over the three axes (0, 1, 2)\n",
    "        if image.shape[axis] < target_size[axis]:\n",
    "            image = add_padding(image, target_size[axis], axis=axis)\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def spatial_normalize_dir(root_directory, target_size, target_spacing, target_origin, target_direction, interp_type=3, verbose=False):\n",
    "    for dirpath, dirs, files in os.walk(root_directory):  # Iterate over root_directory\n",
    "        for filename in files:  # For each file\n",
    "            if not filename.endswith('.nii.gz') and not filename.endswith('.nii'):  # Check if file is a NIFTI file\n",
    "                continue\n",
    "\n",
    "            if verbose:\n",
    "                print(f'Performing spatial normalization: {filename}')\n",
    "            \n",
    "            file_path = os.path.join(dirpath, filename)\n",
    "            image = ants.image_read(file_path)  # Read NIFTI file\n",
    "            image = add_padding_axis(image, target_size)  # Just for GM T2w\n",
    "            resampled_img = spatial_normalization(image, target_size, target_spacing, target_origin, target_direction, interp_type)\n",
    "            ants.image_write(resampled_img, file_path)  # Save the resampled image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_size = (240, 240, 155)\n",
    "target_spacing = (1.0, 1.0, 1.0)\n",
    "target_origin = (-0.0, -239.0, 0.0)\n",
    "target_direction = [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# spatial_normalize_dir(\n",
    "#     GM_DIR_SpN,\n",
    "#     target_size=target_size,\n",
    "#     target_spacing=target_spacing,\n",
    "#     target_origin=target_origin,\n",
    "#     target_direction=target_direction,\n",
    "#     verbose=True\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "GM_DIR_PROCESSED = f'{GM_ROOT}/GM-PROCESSED'  # Normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_scans_by_patient(root_directory):\n",
    "    results = {}\n",
    "    for dirpath, dirs, files in os.walk(root_directory):  # Iterate over root_directory\n",
    "        if 'anat' in dirs or 'derivatives' in dirpath:\n",
    "            continue\n",
    "        for dir in dirs:\n",
    "            if dir == 'derivatives': continue\n",
    "            results[dir] = get_nifti_paths(os.path.join(dirpath, dir))\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_brain_images_dir(\n",
    "    root_directory,\n",
    "    truncate_intensity=(0.01, 0.99),\n",
    "    template_transform_type='Affine',\n",
    "    do_brain_extraction=True,\n",
    "    do_bias_correction=True,\n",
    "    do_denoising=True,\n",
    "    intensity_matching_type=None,\n",
    "    reference_image=None,\n",
    "    intensity_normalization_type=None,\n",
    "    antsxnet_cache_directory=None,\n",
    "    segment=True,\n",
    "    verbose=1,\n",
    "    **kwargs\n",
    "):\n",
    "    nifti_paths = get_scans_by_patient(root_directory)\n",
    "\n",
    "    for patient, scanner_paths in nifti_paths.items():\n",
    "        paths = [path[0] for path in scanner_paths.values()]\n",
    "        image_t1w, image_t1w_ce, image_t2w, image_flair = list(map(ants.image_read, paths))\n",
    "        template = image_t1w_ce\n",
    "        \n",
    "        results = preprocess_brain_images(\n",
    "            image_t1w=image_t1w,\n",
    "            image_t1w_ce=image_t1w_ce,\n",
    "            image_t2w=image_t2w,\n",
    "            image_flair=image_flair,\n",
    "            reference_modality='T1w_ce',\n",
    "            truncate_intensity=truncate_intensity,\n",
    "            do_brain_extraction=do_brain_extraction,\n",
    "            template_transform_type=template_transform_type, # Rigid / Affine / SyN\n",
    "            template=template, # tocheck\n",
    "            do_bias_correction=do_bias_correction,\n",
    "            return_bias_field=False,\n",
    "            do_denoising=do_denoising,\n",
    "            intensity_matching_type=intensity_matching_type,\n",
    "            reference_image=reference_image,\n",
    "            intensity_normalization_type=intensity_normalization_type,\n",
    "            antsxnet_cache_directory=antsxnet_cache_directory,\n",
    "            verbose=verbose,\n",
    "            **kwargs\n",
    "        )\n",
    "        if verbose:\n",
    "            print(f'\\nSaving {patient} MR images...\\n')\n",
    "    \n",
    "        for modality in scanner_paths.keys():\n",
    "            derivatives_dir = f'{root_directory}/derivatives/{patient}'\n",
    "            Path(derivatives_dir).mkdir(parents=True, exist_ok=True)\n",
    "            preprocessed_image = results[modality]['preprocessed_image']\n",
    "            brain_mask = results[modality]['brain_mask']\n",
    "            if segment:\n",
    "                preprocessed_image = segment_image(preprocessed_image, brain_mask)\n",
    "            ants.image_write(preprocessed_image, scanner_paths[modality][0])\n",
    "            ants.image_write(brain_mask, f'{derivatives_dir}/{patient}_desc-{modality}_brain_mask.nii.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocess_brain_images_dir(\n",
    "#     GM_DIR_PROCESSED,\n",
    "#     truncate_intensity=(0.01, 0.99),\n",
    "#     template_transform_type='Affine',  # SyN\n",
    "#     do_brain_extraction=True,\n",
    "#     do_bias_correction=True,\n",
    "#     do_denoising=True,\n",
    "#     segment=True\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **2. SKULL STRIPPING**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def skull_stripping_dir(root_directory, quantile_values=None, segment=True, verbose=False):\n",
    "    for dirpath, dirs, files in os.walk(root_directory):\n",
    "        for filename in files:\n",
    "            if not filename.endswith('.nii.gz') and not filename.endswith('.nii'):\n",
    "                continue\n",
    "            if filename.endswith('T1w.nii.gz'):  # T1w / T1w + contrast\n",
    "                brain_extraction_modality = 't1'\n",
    "            elif filename.endswith('T2w.nii.gz'):  # T2w\n",
    "                brain_extraction_modality = 't2'            \n",
    "            elif filename.endswith('FLAIR.nii.gz'):  # FLAIR\n",
    "                brain_extraction_modality = 'flair'\n",
    "            else:\n",
    "                raise ValueError(f'Unknown modality: {filename}')\n",
    "                \n",
    "            file_path = os.path.join(dirpath, filename)\n",
    "            mask_path = get_mask_path(root_directory, filename)\n",
    "            image = ants.image_read(file_path)\n",
    "            \n",
    "            if quantile_values is not None:\n",
    "                image = truncate_intensities(image, quantile_values=quantile_values)\n",
    "\n",
    "            brain_mask = extract_brain(image, brain_extraction_modality=brain_extraction_modality)\n",
    "            \n",
    "            if segment:\n",
    "                if verbose:\n",
    "                    print(f'Removing skull from: {filename} ({brain_extraction_modality.upper()})')\n",
    "                skull_stripped_img = segment_image(image, brain_mask)\n",
    "                ants.image_write(skull_stripped_img, file_path)\n",
    "\n",
    "            if verbose:\n",
    "                print(f'Saving brain mask: {mask_path}')\n",
    "            \n",
    "            ants.image_write(brain_mask, mask_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# skull_stripping_dir(GM_DIR_SkS, quantile_values=(0.01, 0.99), segment=False, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **3. BIAS FIELD CORRECTION AND DENOISING**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bias_correction_and_denoising_dir(root_directory, verbose=False):\n",
    "    for dirpath, dirs, files in os.walk(root_directory):\n",
    "        if 'derivatives' in dirpath:\n",
    "            continue\n",
    "        for filename in files:\n",
    "            if not filename.endswith('.nii.gz') and not filename.endswith('.nii'):\n",
    "                continue\n",
    "                \n",
    "            file_path = os.path.join(dirpath, filename)\n",
    "            mask_path = get_mask_path(root_directory, filename)\n",
    "            image = ants.image_read(file_path)\n",
    "            mask = ants.image_read(mask_path)\n",
    "\n",
    "            if verbose:\n",
    "                print(f'Performing bias field correction and denoising: {filename} ({mask_path.split(\"/\")[-1]})')\n",
    "            \n",
    "            preprocessed_img = bias_field_correction(image, mask=mask)\n",
    "            preprocessed_img = denoise_image(preprocessed_img, mask=mask)\n",
    "            preprocessed_img = segment_image(preprocessed_img, mask=mask)\n",
    "            ants.image_write(preprocessed_img, file_path)  # Save the resampled image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bias_correction_and_denoising_dir(GM_DIR_BFC_DN, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **4. INTENSITY MATCHING**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **4.1. SAVE HISTOGRAMS**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_histogram(img_path, bins=100):\n",
    "    img = nib.load(img_path).get_fdata()\n",
    "    hist, _ = np.histogram(img, bins=bins)\n",
    "    return hist  # / np.sum(hist)\n",
    "\n",
    "def save_histogram(img_path):\n",
    "    hist_path = img_path[:-7] + '.npy'\n",
    "    if os.path.exists(hist_path):\n",
    "        return\n",
    "    try:\n",
    "        hist = calculate_histogram(img_path)\n",
    "    except ValueError:\n",
    "        return\n",
    "    \n",
    "    np.save(hist_path, hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_histograms(root_directory, verbose=False):\n",
    "    for dirpath, dirs, files in os.walk(root_directory):\n",
    "        if 'derivatives' in dirpath:\n",
    "            continue\n",
    "            \n",
    "        for filename in files:\n",
    "            if not filename.endswith('.nii.gz') and not filename.endswith('.nii'):\n",
    "                continue\n",
    "                \n",
    "            path = os.path.join(dirpath, filename)\n",
    "            \n",
    "            if verbose:\n",
    "                print(f'Saving histogram for: {filename}')\n",
    "                \n",
    "            save_histogram(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save_histograms(GM_DIR_BFC_DN, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save_histograms(UCSF_BASE, verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **4.2. HISTOGRAM MATCHING**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def match_histograms(root_directory, ref_directory, num_clusters=3, verbose=False):\n",
    "    img_paths = get_nifti_paths(root_directory)        \n",
    "    ref_img_paths = get_nifti_paths(ref_directory)\n",
    "\n",
    "    # img_histograms = load_histograms(root_directory)\n",
    "    ref_histograms = load_histograms(ref_directory)\n",
    "    nifti_paths = get_nifti_paths(ref_directory)\n",
    "    ref_histograms = {\n",
    "        modality: [calculate_histogram(nifti) for (nifti) in nifti_paths[modality]]\n",
    "        for modality in ('T1w', 'T1w_ce', 'T2w', 'FLAIR')\n",
    "    }\n",
    "    kmeans = {modality: cluster_histograms(ref_histograms[modality], num_clusters) for modality in ('T1w', 'T1w_ce', 'T2w', 'FLAIR')}\n",
    "    \n",
    "    for modality, img_mod_paths in img_paths.items():\n",
    "        if verbose:\n",
    "            print(f'Matching {modality} images')\n",
    "        for img_path in img_mod_paths:\n",
    "            matched_img = match_histogram(img_path, ref_histograms[modality], kmeans[modality], ref_img_paths[modality], verbose=verbose)\n",
    "            ants.image_write(matched_img, img_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GM_DIR_HM = f'{GM_ROOT}/GM-BRATS+HM'\n",
    "\n",
    "# match_histograms(GM_DIR_HM, UCSF_BASE, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **5. INTENSITY NORMALIZATION**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_intensities_dir(root_directory, use_masks=True, modality_in_mask_name=False, verbose=False, **kwargs):\n",
    "    for dirpath, dirs, files in os.walk(root_directory):\n",
    "        for filename in files:\n",
    "            if not filename.endswith('.nii.gz') and not filename.endswith('.nii'):\n",
    "                continue\n",
    "            \n",
    "            if filename.endswith('ce-GADOLINIUM_T1w.nii.gz'):\n",
    "                modality, normalization_type = 'T1w_ce', kwargs.get('T1w_ce')\n",
    "            elif filename.endswith('T1w.nii.gz'):\n",
    "                modality, normalization_type = 'T1w', kwargs.get('T1w')\n",
    "            elif filename.endswith('T2w.nii.gz'):\n",
    "                modality, normalization_type = 'T2w', kwargs.get('T2w')\n",
    "            elif filename.endswith('FLAIR.nii.gz'):\n",
    "                modality, normalization_type = 'FLAIR', kwargs.get('FLAIR')\n",
    "            else:\n",
    "                continue\n",
    "\n",
    "            if verbose:\n",
    "                print(f'Normalizing intensity for: {filename}')\n",
    "            \n",
    "            file_path = os.path.join(dirpath, filename)\n",
    "            image = ants.image_read(file_path)\n",
    "            mask = None\n",
    "            \n",
    "            if use_masks:\n",
    "                mask_path = get_mask_path(root_directory, filename, modality_in_name=modality_in_mask_name)\n",
    "                mask = nib.load(mask_path).get_fdata()\n",
    "\n",
    "            norm_img = normalize_intensity(image, mask=mask, modality=modality, normalization_type=normalization_type)\n",
    "            ants.image_write(norm_img, file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "GM_BRATS_NORM = f'{GM_ROOT}/GM-BRATS+HM+NORM'  # Bias Field Correction + Denoise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize_intensities_dir(\n",
    "#     GM_BRATS_NORM,\n",
    "#     T1w='WhiteStripe',\n",
    "#     T1w_ce='WhiteStripe',\n",
    "#     T2w='WhiteStripe',\n",
    "#     FLAIR='WhiteStripe',\n",
    "#     verbose=True\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize_intensities_dir(\n",
    "#     UCSF_NORM,\n",
    "#     T1w='WhiteStripe',\n",
    "#     T1w_ce='WhiteStripe',\n",
    "#     T2w='WhiteStripe',\n",
    "#     FLAIR='WhiteStripe',\n",
    "#     verbose=False,\n",
    "#     modality_in_mask_name=False\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **6. TUMOR CROPPING**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "GM_CROPPED = f'{GM_ROOT}/GM-BRATS+HM+NORM+CROPPED'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adjust_interval(start, end, desired_length, lower_limit=0, upper_limit=239):\n",
    "    # Compute current length of interval\n",
    "    N = end - start\n",
    "    \n",
    "    # Compute half the difference to adjust\n",
    "    half_difference = (desired_length - N) / 2\n",
    "    \n",
    "    # Adjust the start and end\n",
    "    new_start = start - half_difference\n",
    "    new_end = end + half_difference\n",
    "    \n",
    "    # Adjust for constraints\n",
    "    if new_start < lower_limit:\n",
    "        new_end += (lower_limit - new_start)\n",
    "        new_start = lower_limit\n",
    "    if new_end > upper_limit:\n",
    "        new_start -= (new_end - upper_limit)\n",
    "        new_end = upper_limit\n",
    "    \n",
    "    return int(new_start), int(new_end)\n",
    "\n",
    "def divide_interval(start, end, N, lower_limit=0, upper_limit=154):\n",
    "    # Create dividers within the interval\n",
    "    interval_length = (end - start) / N\n",
    "    # Create dividers using a list comprehension, skip the end point\n",
    "    dividers = list(sorted(set([int(start + i*interval_length) for i in range(0, N)])))\n",
    "        \n",
    "    step = int(np.ceil(interval_length))\n",
    "    # If there's room to the left, prepend values to the left of the start point\n",
    "    low, high = start, end\n",
    "    while len(dividers) < N:\n",
    "        if not(start < lower_limit):\n",
    "            low -= step\n",
    "            dividers.insert(0, low)\n",
    "            if len(dividers) == N: break\n",
    "        if not(end > upper_limit - 1):\n",
    "            high += step\n",
    "            if high > upper_limit: continue\n",
    "            dividers.append(high)\n",
    "    \n",
    "    assert len(dividers) == N, (len(dividers), (start, end))\n",
    "    return dividers\n",
    "\n",
    "def extract_slices_from_bbox(image, bbox, resize_to=None, n_slices=16):\n",
    "    (xmin, xmax), (ymin, ymax), (zmin, zmax) = bbox\n",
    "    \n",
    "    zmin = 0   if zmin < 0   else zmin\n",
    "    zmax = 154 if zmax > 154 else zmax\n",
    "    # print(interval_length(bbox))\n",
    "\n",
    "    if resize_to:\n",
    "        final_x, final_y = resize_to\n",
    "        xmin, xmax = adjust_interval(xmin, xmax, final_x)\n",
    "        ymin, ymax = adjust_interval(ymin, ymax, final_y)\n",
    "        # print((xmin, xmax), (ymin, ymax), 'adjusted')\n",
    "    \n",
    "    z_values = divide_interval(zmin, zmax, n_slices)\n",
    "    # print(z_values, len(z_values))\n",
    "\n",
    "    if resize_to:\n",
    "        # imgs = []\n",
    "        # for i, n in enumerate(z_values):\n",
    "        #     print((xmin, xmax), (ymin, ymax), (zmin, zmax), i, n)\n",
    "        #     imgs.append(image[xmin:xmax, ymin:ymax, n])\n",
    "        #     print(z_values)\n",
    "        # return imgs\n",
    "        # print([((xmin, xmax), (ymin, ymax), i, n) for i, n in enumerate(z_values)])\n",
    "        return [image[xmin:xmax, ymin:ymax, n] for n in z_values]\n",
    "    return [image[:, :, n] for n in z_values]\n",
    "\n",
    "\n",
    "def merge_slices_to_volume(numpy_slices):\n",
    "    # Stack arrays along the third dimension to create a 3D volume\n",
    "    volume_array = np.stack(numpy_slices, axis=-1)\n",
    "    # Convert numpy array back to an antspy image\n",
    "    merged_volume = ants.from_numpy(volume_array)\n",
    "    return merged_volume\n",
    "\n",
    "def crop_tumor(image, segmentation, modality, n_slices=16, n_pixels=16, scale_factor=None, resize_to=None):\n",
    "    bbox = compute_bounding_cube(segmentation)\n",
    "    # print(bbox)\n",
    "    scaled_bbox = scale_bounding_cube(bbox, n_pixels=n_pixels, factor=scale_factor)\n",
    "    # print(scaled_bbox)\n",
    "    img_slices = extract_slices_from_bbox(image, scaled_bbox, n_slices=n_slices, resize_to=resize_to)\n",
    "    # print(img_slices)\n",
    "    main_images = merge_slices_to_volume(img_slices)\n",
    "    return main_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# t = divide_interval(99, 148, 64)\n",
    "# print(t, len(t), len(set(t)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# xs, ys, zs = test(paths)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **GM Dataset**\n",
    " * Min: (42, 37, 35)\n",
    " * Max: (115, 136, 126)\n",
    "\n",
    "### **TCIA Dataset**\n",
    " * Min: (18, 18, 16)\n",
    " * Max: (179, 198, 150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(sorted(xs))\n",
    "# print(max(xs))\n",
    "# print(min(xs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # print(sorted(ys))\n",
    "# print(max(ys))\n",
    "# print(min(ys))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # print(sorted(zs))\n",
    "# print(max(zs))\n",
    "# print(min(zs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# UCSF_CROPPED = f'{UCSF_ROOT}/UCSF-NORM-LIGHT'\n",
    "# for dirpath, dirs, files in os.walk(UCSF_CROPPED):\n",
    "#     for filename in files:\n",
    "#         if not filename.endswith('.nii.gz') and not filename.endswith('.nii'):\n",
    "#             continue\n",
    "        \n",
    "#         if filename.endswith('ce-GADOLINIUM_T1w.nii.gz'):\n",
    "#             continue\n",
    "#         elif filename.endswith('T1w.nii.gz'):\n",
    "#             file_path = os.path.join(dirpath, filename)\n",
    "#             # print(file_path)\n",
    "#             os.remove(file_path)\n",
    "#         elif filename.endswith('T2w.nii.gz'):\n",
    "#             file_path = os.path.join(dirpath, filename)\n",
    "#             # print(file_path)\n",
    "#             os.remove(file_path)\n",
    "#         elif filename.endswith('FLAIR.nii.gz'):\n",
    "#             continue\n",
    "#         else:\n",
    "#             continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "GM_CROPPED = f'{GM_ROOT}/GM-BRATS+HM+NORM+CROPPED'\n",
    "UCSF_CROPPED = f'{UCSF_ROOT}/UCSF-NORM+CROPPED'\n",
    "UCSF_CROPPED = f'{UCSF_ROOT}/UCSF-NORM-LIGHT-CROPPED'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def crop_tumor_dir(root_directory, resize_to=(128, 128), n_slices=64, verbose=True):\n",
    "    paths = get_nifti_paths(root_directory, ommit_derivatives=True)\n",
    "\n",
    "    for modality, file_paths in paths.items():\n",
    "        for file_path in file_paths:\n",
    "            patient_no = get_patient_no(file_path)\n",
    "\n",
    "            # if patient_no <= 12:\n",
    "            #     continue\n",
    "            image = ants.image_read(file_path)\n",
    "            segmentation = load_mask(root_directory, patient_no)\n",
    "            \n",
    "            if verbose:\n",
    "                print(f'Cropping tumor from: {os.path.basename(file_path)}')\n",
    "            \n",
    "            cropped_image = crop_tumor(\n",
    "                image=image,\n",
    "                segmentation=segmentation,\n",
    "                modality=modality,\n",
    "                resize_to=resize_to,\n",
    "                n_slices=n_slices,\n",
    "            )\n",
    "            # print(image.shape, cropped_image.shape)\n",
    "            assert cropped_image.shape == (*resize_to, n_slices)\n",
    "            ants.image_write(cropped_image, file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "crop_tumor_dir(GM_CROPPED, resize_to=(128, 128), n_slices=64, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cropping tumor from: sub-0004_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0005_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0007_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0008_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0009_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0010_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0011_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0012_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0013_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0014_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0015_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0016_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0017_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0018_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0019_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0020_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0021_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0022_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0023_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0024_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0025_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0026_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0027_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0029_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0030_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0031_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0032_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0033_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0034_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0035_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0036_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0037_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0038_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0039_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0040_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0041_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0042_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0043_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0044_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0045_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0046_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0047_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0048_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0049_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0050_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0053_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0055_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0056_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0057_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0058_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0059_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0061_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0063_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0064_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0065_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0066_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0067_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0068_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0069_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0070_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0071_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0072_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0073_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0074_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0075_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0076_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0077_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0078_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0079_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0080_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0082_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0083_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0084_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0085_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0086_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0087_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0088_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0089_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0090_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0091_ce-GADOLINIUM_T1w.nii.gz\n",
      "Cropping tumor from: sub-0092_ce-GADOLINIUM_T1w.nii.gz\n",
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      "Cropping tumor from: sub-0540_FLAIR.nii.gz\n"
     ]
    }
   ],
   "source": [
    "crop_tumor_dir(UCSF_CROPPED, resize_to=(128, 128), n_slices=64, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **7. CONVERT DATA TO .NPY**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# UCSF_NORM_NP = f'{UCSF_ROOT}/UCSF-NORM-NPZ'\n",
    "GM_NORM_NP = f'{GM_ROOT}/GM-BRATS+HM+NORM-NPZ'\n",
    "\n",
    "NORM_DIR = GM_NORM_NP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nifti_dir_to_numpy(root_directory, overwrite=True, ommit_derivatives=False, verbose=True, format_='.npy'):\n",
    "    paths = []\n",
    "    for dirpath, dirs, files in os.walk(root_directory):\n",
    "        if ommit_derivatives and 'derivatives' in dirpath:\n",
    "            continue\n",
    "        for filename in files:\n",
    "            if filename.endswith('.nii.gz') or filename.endswith('.nii'):\n",
    "                paths.append(os.path.join(dirpath, filename))\n",
    "\n",
    "    for file_path in paths:\n",
    "        np_file = file_path.split('.')[0] + format_\n",
    "        nifti = load_nifti(file_path, read_with='nibabel')\n",
    "\n",
    "        if verbose: print(f'Saving {np_file}')\n",
    "        if format_ == '.npy':\n",
    "            np.save(np_file, nifti)\n",
    "        elif format_ == '.npz':\n",
    "            np.savez(np_file, nifti)\n",
    "        if overwrite: os.remove(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving /mnt/c/Users/sergio/Desktop/TFG/GM/GM-BRATS+HM+NORM-NPZ/derivatives/sub-0000/sub-0000_desc-brain_mask.npz\n",
      "Saving /mnt/c/Users/sergio/Desktop/TFG/GM/GM-BRATS+HM+NORM-NPZ/derivatives/sub-0000/sub-0000_desc-tumor_dseg.npz\n",
      "Saving /mnt/c/Users/sergio/Desktop/TFG/GM/GM-BRATS+HM+NORM-NPZ/derivatives/sub-0001/sub-0001_desc-brain_mask.npz\n",
      "Saving /mnt/c/Users/sergio/Desktop/TFG/GM/GM-BRATS+HM+NORM-NPZ/derivatives/sub-0001/sub-0001_desc-tumor_dseg.npz\n",
      "Saving /mnt/c/Users/sergio/Desktop/TFG/GM/GM-BRATS+HM+NORM-NPZ/derivatives/sub-0002/sub-0002_desc-brain_mask.npz\n",
      "Saving /mnt/c/Users/sergio/Desktop/TFG/GM/GM-BRATS+HM+NORM-NPZ/derivatives/sub-0002/sub-0002_desc-tumor_dseg.npz\n",
      "Saving /mnt/c/Users/sergio/Desktop/TFG/GM/GM-BRATS+HM+NORM-NPZ/derivatives/sub-0003/sub-0003_desc-brain_mask.npz\n",
      "Saving /mnt/c/Users/sergio/Desktop/TFG/GM/GM-BRATS+HM+NORM-NPZ/derivatives/sub-0003/sub-0003_desc-tumor_dseg.npz\n",
      "Saving /mnt/c/Users/sergio/Desktop/TFG/GM/GM-BRATS+HM+NORM-NPZ/derivatives/sub-0004/sub-0004_desc-brain_mask.npz\n",
      "Saving /mnt/c/Users/sergio/Desktop/TFG/GM/GM-BRATS+HM+NORM-NPZ/derivatives/sub-0004/sub-0004_desc-tumor_dseg.npz\n",
      "Saving /mnt/c/Users/sergio/Desktop/TFG/GM/GM-BRATS+HM+NORM-NPZ/derivatives/sub-0005/sub-0005_desc-brain_mask.npz\n",
      "Saving /mnt/c/Users/sergio/Desktop/TFG/GM/GM-BRATS+HM+NORM-NPZ/derivatives/sub-0005/sub-0005_desc-tumor_dseg.npz\n",
      "Saving /mnt/c/Users/sergio/Desktop/TFG/GM/GM-BRATS+HM+NORM-NPZ/derivatives/sub-0006/sub-0006_desc-brain_mask.npz\n",
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     ]
    }
   ],
   "source": [
    "nifti_dir_to_numpy(NORM_DIR, overwrite=True, verbose=True, format_='.npz')"
   ]
  }
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