{

 "cells": [

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "import os\n",

    "import re\n",

    "import numpy as np\n",

    "from rt_utils import RTStructBuilder\n",

    "import matplotlib.pyplot as plt\n",

    "import pydicom \n",

    "from PIL import Image\n",

    "CT_PATH = './CT_Slices'"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "def transform_to_hu(medical_image):\n",

    "    intercept = medical_image.RescaleIntercept\n",

    "    slope = medical_image.RescaleSlope\n",

    "    image = medical_image.pixel_array\n",

    "    hu_image = image * slope + intercept\n",

    "\n",

    "    return hu_image\n",

    "\n",

    "def apply_windowing(ds, window_center, window_width):\n",

    "    # Load the DICOM file\n",

    "    \n",

    "    # Get the pixel data and rescale to Hounsfield units (HU)\n",

    "    pixel_array = ds.pixel_array.astype(np.float32)\n",

    "    intercept = ds.RescaleIntercept\n",

    "    slope = ds.RescaleSlope\n",

    "    hu_array = pixel_array * slope + intercept\n",

    "    \n",

    "    # Apply windowing\n",

    "    window_min = window_center - (window_width / 2)\n",

    "    window_max = window_center + (window_width / 2)\n",

    "    windowed_array = np.clip(hu_array, window_min, window_max)\n",

    "    \n",

    "    # Normalize the windowed array to [0, 1]\n",

    "    normalized_array = (windowed_array - window_min) / (window_max - window_min)\n",

    "    \n",

    "    return normalized_array\n",

    "\n",

    "\n",

    "def set_window(img, hu=[-800.,1200.]):\n",

    "    window = np.array(hu)\n",

    "    newimg = (img-window[0]) / (window[1]-window[0])\n",

    "    newimg[newimg < 0] = 0\n",

    "    newimg[newimg > 1] = 1\n",

    "    newimg = (newimg * 255).astype('uint8')\n",

    "    return newimg\n",

    "\n",

    "\n",

    "def zero_center(hu_image):\n",

    "    hu_image = hu_image - np.mean(hu_image)\n",

    "    return hu_image"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "def rename_folders(path):\n",

    "    for patient in os.listdir(path):\n",

    "        old_path = os.path.join(path, patient)\n",

    "        new_name = re.sub(r'[^0-9]','',patient)\n",

    "        new_path = os.path.join(path, new_name)\n",

    "        os.rename(old_path, new_name)\n",

    "        \n",

    "rename_folders(CT_PATH)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "DATA_PATH = './data/'"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "import shutil\n",

    "def create_ds_structure(path):\n",

    "    for patient in os.listdir(path):\n",

    "        p_path = os.path.join(path, patient)\n",

    "        dicom_path = os.path.join(p_path, 'dicom')\n",

    "        r_path = os.path.join(p_path, 'r')\n",

    "        if not os.path.exists(dicom_path):\n",

    "            os.mkdir(dicom_path)\n",

    "        if not os.path.exists(r_path):\n",

    "            os.mkdir(r_path)\n",

    "        for file in os.listdir(p_path):\n",

    "            file_path = os.path.join(p_path, file)\n",

    "            if os.path.isfile(file_path):\n",

    "                if(file[0:2]=='CT'):\n",

    "                    new_path = os.path.join(dicom_path, file)\n",

    "                    shutil.move(file_path, new_path)\n",

    "                if(file[0:2]=='RS'):\n",

    "                    new_path = os.path.join(r_path, file)\n",

    "                    shutil.move(file_path, new_path)\n",

    "\n",

    "        \n",

    "\n",

    "create_ds_structure(DATA_PATH)    \n",

    "\n",

    "\n"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "\n",

    "\n",

    "# Load existing RT Struct. Requires the series path and existing RT Struct path\n",

    "rtstruct = RTStructBuilder.create_from(\n",

    "  dicom_series_path=\"./data/01/dicom\", \n",

    "  rt_struct_path=\"./data/01/r/RS.1.2.410.200018.1001.1.3.89143765.2.20151123111228992.dcm\"\n",

    ")\n",

    "\n",

    "# View all of the ROI names from within the image\n",

    "print(rtstruct.get_roi_names())\n",

    "ds = []\n",

    "for image in os.listdir('./data/01/dicom/'):\n",

    "  img_path = os.path.join('./data/01/dicom/', image)\n",

    "  ds.append(pydicom.dcmread(img_path))\n",

    "\n",

    "\n",

    "\n",

    "rtstruct"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "img_idx = 33\n",

    "mask_idx = 44 -img_idx\n",

    "ds_img = apply_windowing(ds[img_idx], 35, 350)\n",

    "# Loading the 3D Mask from within the RT Struct\n",

    "mask_3d = rtstruct.get_roi_mask_by_name(\"heart\")\n",

    "\n",

    "\n",

    "fliped = np.flip(mask_3d, axis=2)\n",

    "# Display one slice of the region\n",

    "first_mask_slice = fliped[:, :, img_idx]\n",

    "plt.figure(figsize = (5,5))\n",

    "plt.imshow(first_mask_slice)\n",

    "#plt.imshow(ds_img, cmap='gray',alpha=0.9)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "IMG_PATH = './2d_data/images/'\n",

    "MASKS_PATH = './2d_data/masks/'\n",

    "def create_2d_dataset(path, img_path_save, mask_path_save):\n",

    "    for patient in os.listdir(path):\n",

    "        p_path = os.path.join(path, patient)\n",

    "\n",

    "        dicom_path = os.path.join(p_path, 'dicom')\n",

    "        \n",

    "        r_path = os.path.join(p_path, 'r')\n",

    "\n",

    "        r_file = os.listdir(r_path)[0]\n",

    "        r_file = os.path.join(r_path, r_file)\n",

    "\n",

    "\n",

    "        try: \n",

    "            rtstruct = RTStructBuilder.create_from(\n",

    "            dicom_series_path=dicom_path, \n",

    "            rt_struct_path=r_file\n",

    "            )   \n",

    "            mask_3d = rtstruct.get_roi_mask_by_name(\"heart\")\n",

    "            fliped = np.flip(mask_3d, axis=2)\n",

    "            idx = 0\n",

    "            for dcm_file in os.listdir(dicom_path):\n",

    "                dcm_file_path = os.path.join(dicom_path, dcm_file)\n",

    "                new_dcm_file = os.path.join(img_path_save,(patient+'_'+dcm_file))\n",

    "\n",

    "                mask_filename = new_dcm_file.replace('.dcm', '_mask.png')\n",

    "                mask_filename = mask_filename.replace('images', 'masks')\n",

    "                #print(mask_filename)\n",

    "                mask_tosave = Image.fromarray(fliped[:,:,idx])\n",

    "                if not os.path.exists(new_dcm_file):\n",

    "                    shutil.copy(dcm_file_path, new_dcm_file)\n",

    "                if not os.path.exists(mask_filename):\n",

    "                    mask_tosave.save(mask_filename)\n",

    "\n",

    "                idx+=1\n",

    "        except: \n",

    "            print(f'Skipping patient {patient}')\n",

    "        \n",

    "                \n",

    "\n",

    "create_2d_dataset(DATA_PATH, IMG_PATH, MASKS_PATH)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": []

  }

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