preprocess_rsna.ipynb

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    "#\n",
    "# Code based on https://www.kaggle.com/code/paulbacher/custom-preprocessor-rsna-breast-cancer\n",
    "#\n",
    "\n",
    "import os\n",
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cv2\n",
    "import pydicom\n",
    "import dicomsdl\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm.notebook import tqdm, trange\n",
    "from joblib import Parallel, delayed\n",
    "import gc"
   ]
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    "csv_path = '/mnt/striped/kaggle/rsna_bcd/data/train.csv'\n",
    "train_path = '/mnt/striped/kaggle/rsna_bcd/data/train_images'\n",
    "data = pd.read_csv(csv_path)"
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       "['/mnt/striped/kaggle/rsna_bcd/data/train_images/58224/132390955.dcm',\n",
       " '/mnt/striped/kaggle/rsna_bcd/data/train_images/21809/1307476428.dcm',\n",
       " '/mnt/striped/kaggle/rsna_bcd/data/train_images/58351/1689606258.dcm',\n",
       " '/mnt/striped/kaggle/rsna_bcd/data/train_images/60826/1465194139.dcm',\n",
       " '/mnt/striped/kaggle/rsna_bcd/data/train_images/1250/1329687627.dcm']"
      ]
     },
     "execution_count": 3,
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   "source": [
    "def get_paths(n: int=len(data), shuffle: bool=False):\n",
    "    if shuffle == True:\n",
    "        df = data.sample(frac=1, random_state=0)\n",
    "    else:\n",
    "        df = data\n",
    "    paths = []\n",
    "    ids_cache = []\n",
    "    for i in range(n):\n",
    "        patient = str(df.iloc[i]['patient_id'])\n",
    "        scan = str(df.iloc[i]['image_id'])\n",
    "        paths.append(train_path + '/' + patient + '/' + scan + '.dcm')\n",
    "        ids_cache.append({'patient_id': patient, 'scan_id': scan})\n",
    "    return paths, ids_cache\n",
    "\n",
    "\n",
    "# Example\n",
    "paths, _ = get_paths(n=5, shuffle=True)\n",
    "paths"
   ]
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     "text": [
      "Ratios: [1.3, 1.1985370950888192, 1.2307692307692308, 1.279030910609858, 1.0895218718209563]\n",
      "Min: 1.0895218718209563\n",
      "Max: 1.3\n",
      "Avg: 1.219571821657773\n"
     ]
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   "source": [
    "def calculate_aspect_ratios(paths: list, preprocessor=None):\n",
    "    ratios = []\n",
    "    for i in trange(len(paths)):\n",
    "        if preprocessor:\n",
    "            img = preprocessor.preprocess_single_image(paths[i])\n",
    "        else:\n",
    "            scan = pydicom.dcmread(paths[i])\n",
    "            img = scan.pixel_array\n",
    "        height, width = img.shape\n",
    "        ratio = height / width\n",
    "        ratios.append(ratio)\n",
    "    return ratios\n",
    "\n",
    "\n",
    "# Example\n",
    "ratios = calculate_aspect_ratios(paths)\n",
    "print(\"Ratios:\", ratios)\n",
    "print(\"Min:\", np.min(ratios))\n",
    "print(\"Max:\", np.max(ratios))\n",
    "print(\"Avg:\", np.mean(ratios))"
   ]
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   "source": [
    "def image_resize(image, width = None, height = None, inter = cv2.INTER_NEAREST):\n",
    "    # initialize the dimensions of the image to be resized and\n",
    "    # grab the image size\n",
    "    dim = None\n",
    "    (h, w) = image.shape[:2]\n",
    "\n",
    "    # if both the width and height are None, then return the\n",
    "    # original image\n",
    "    if width is None and height is None:\n",
    "        return image\n",
    "\n",
    "    # check to see if the width is None\n",
    "    if width is None:\n",
    "        # calculate the ratio of the height and construct the\n",
    "        # dimensions\n",
    "        r = height / float(h)\n",
    "        dim = (int(w * r), height)\n",
    "\n",
    "    # otherwise, the height is None\n",
    "    else:\n",
    "        # calculate the ratio of the width and construct the\n",
    "        # dimensions\n",
    "        r = width / float(w)\n",
    "        dim = (width, int(h * r))\n",
    "\n",
    "    # resize the image\n",
    "    resized = cv2.resize(image, dim, interpolation = inter)\n",
    "\n",
    "    # return the resized image\n",
    "    return resized\n",
    "\n",
    "class MammographyPreprocessor():\n",
    "    \n",
    "    # Constructor\n",
    "    def __init__(self, size: tuple=None, breast_side: str='L',\n",
    "                 csv_path=None, train_path=None, save_root=None):\n",
    "        self.size = size\n",
    "        os.makedirs(os.getcwd(), exist_ok=True)\n",
    "        self.breast_side = breast_side\n",
    "        assert breast_side in ['L', 'R'], \"breast_side should be 'L' or 'R'\"\n",
    "        \n",
    "        # implement the paths of the original RSNA dataset (V2)\n",
    "        if csv_path:\n",
    "            self.csv_path = csv_path\n",
    "            \n",
    "        if train_path:\n",
    "            self.train_path = train_path\n",
    "            \n",
    "        self.df = pd.read_csv(self.csv_path)\n",
    "        \n",
    "        self.save_root = save_root\n",
    "    \n",
    "    # Get the paths from the preprocessor (V2)\n",
    "    def get_paths(self, n: int=None, shuffle: bool=False, return_cache: bool=False):\n",
    "        if n == None:\n",
    "            n = len(self.df)\n",
    "        if shuffle == True:\n",
    "            df = self.df.sample(frac=1, random_state=0).copy()\n",
    "        else:\n",
    "            df = self.df.copy()\n",
    "        paths = []\n",
    "        ids_cache = []\n",
    "        for i in range(n):\n",
    "            patient = str(df.iloc[i]['patient_id'])\n",
    "            scan = str(df.iloc[i]['image_id'])\n",
    "            paths.append(self.train_path + '/' + patient + '/' + scan + '.dcm')\n",
    "            ids_cache.append({'patient_id': patient, 'scan_id': scan})\n",
    "        if return_cache:\n",
    "            return paths, ids_cache\n",
    "        else:\n",
    "            return paths\n",
    "    \n",
    "    # Read from a path and convert to image array\n",
    "    def read_image(self, path: str):\n",
    "        scan = pydicom.dcmread(path)\n",
    "        img = scan.pixel_array\n",
    "        return img\n",
    "    \n",
    "    # Apply the preprocessing methods on one image\n",
    "    def preprocess_single_image(self, path: str, save: bool=False,\n",
    "                                save_dir: str=None, png: bool=True):\n",
    "        scan = dicomsdl.open(path)\n",
    "        img = scan.pixelData()\n",
    "        img = self._windowing(img, scan)\n",
    "        img = self._fix_photometric_interpretation(img, scan)\n",
    "        img = self._normalize_to_255(img)\n",
    "        img = self._flip_breast_side(img)\n",
    "        img = self._crop(img)\n",
    "        if self.size:\n",
    "            img = self._resize(img)\n",
    "        if save:\n",
    "            self._save_image(img, path, png, save_dir)\n",
    "            return # do not return the images to avoid memory leak\n",
    "        return img\n",
    "    \n",
    "    # Preprocess all the images from the paths\n",
    "    def preprocess_all(self, paths: list, save: bool=True,\n",
    "                       save_dir: str='train_images', png: bool=True,\n",
    "                       parallel: bool=False, n_jobs: int=4):\n",
    "        clock = time.time()\n",
    "        if parallel:\n",
    "            Parallel(n_jobs=n_jobs) \\\n",
    "            (delayed(self.preprocess_single_image) \\\n",
    "            (path, save, save_dir, png) for path in tqdm(paths, total=len(paths)))\n",
    "            print(\"Parallel preprocessing done!\")\n",
    "        else:\n",
    "            for i in trange(len(paths)):\n",
    "                self.preprocess_single_image(paths[i], save, save_dir, png)\n",
    "            print(\"Sequential preprocessing done!\")\n",
    "        print(\"Time =\", np.around(time.time() - clock, 3), 'sec')\n",
    "    \n",
    "    # Display the images from the dicom paths with optional preprocessing\n",
    "    def display(self, paths: list, rows: int, cols: int,\n",
    "                preprocess: bool=False, cmap='bone', cbar: bool=False,\n",
    "                save_fig: bool=False, save_name: str='myplot.png'):\n",
    "        assert len(paths) >= (rows * cols), \\\n",
    "        f\"Not enough paths for the display. \" \\\n",
    "        f\"Please give at least {rows * cols} paths.\"\n",
    "        plt.figure(figsize=(18, 26 * rows / cols))\n",
    "        for i in trange(rows * cols):\n",
    "            path = paths[i]\n",
    "            if preprocess:\n",
    "                img = self.preprocess_single_image(path, save=False)\n",
    "            else:\n",
    "                img = self.read_image(path)\n",
    "            plt.subplot(rows, cols, i+1)\n",
    "            plt.imshow(img, cmap=cmap)\n",
    "            if cbar:\n",
    "                plt.colorbar()\n",
    "            plt.grid(False)\n",
    "            plt.title(path.split('/')[-1][:-4])\n",
    "        plt.suptitle(\"Preprocessed images\" if preprocess \\\n",
    "                     else \"Raw images\", fontsize=25)\n",
    "        if save_fig:\n",
    "            plt.savefig(save_name, facecolor='white')\n",
    "        plt.show()\n",
    "    \n",
    "    # Adjust the contrast of an image\n",
    "    def _windowing(self, img, scan):\n",
    "        center = scan.WindowCenter\n",
    "        width = scan.WindowWidth\n",
    "        bits_stored = scan.BitsStored\n",
    "        function = scan.VOILUTFunction\n",
    "        if isinstance(center, list):\n",
    "            center = center[0]\n",
    "        if isinstance(width, list):\n",
    "            width = width[0] \n",
    "        y_range = float(2**bits_stored - 1)\n",
    "        if function == 'SIGMOID':\n",
    "            img = y_range / (1 + np.exp(-4 * (img - center) / width))\n",
    "        else: # LINEAR\n",
    "            center -= 0.5\n",
    "            width -= 1\n",
    "            below = img <= (center - width / 2)\n",
    "            above = img > (center + width / 2)\n",
    "            between = np.logical_and(~below, ~above)\n",
    "            img[below] = 0\n",
    "            img[above] = y_range\n",
    "            img[between] = ((img[between] - center) / width + 0.5) * y_range\n",
    "        return img\n",
    "    \n",
    "    # Interpret pixels in a consistant way\n",
    "    def _fix_photometric_interpretation(self, img, scan):\n",
    "        if scan.PhotometricInterpretation == 'MONOCHROME1':\n",
    "            return img.max() - img\n",
    "        elif scan.PhotometricInterpretation == 'MONOCHROME2':\n",
    "            return img - img.min()\n",
    "        else:\n",
    "            raise ValueError(\"Invalid Photometric Interpretation: {}\"\n",
    "                               .format(scan.PhotometricInterpretation))\n",
    "    \n",
    "    # Cast into 8-bits for saving\n",
    "    def _normalize_to_255(self, img):\n",
    "        if img.max() != 0:\n",
    "            img = img / img.max()\n",
    "        img *= 255\n",
    "        return img.astype(np.uint8)\n",
    "    \n",
    "    # Flip the breast horizontally on the chosen side \n",
    "    def _flip_breast_side(self, img):\n",
    "        img_breast_side = self._determine_breast_side(img)\n",
    "        if img_breast_side == self.breast_side:\n",
    "            return img\n",
    "        else:\n",
    "            return np.fliplr(img)    \n",
    "    \n",
    "    # Determine the current breast side\n",
    "    def _determine_breast_side(self, img):\n",
    "        col_sums_split = np.array_split(np.sum(img, axis=0), 2)\n",
    "        left_col_sum = np.sum(col_sums_split[0])\n",
    "        right_col_sum = np.sum(col_sums_split[1])\n",
    "        if left_col_sum > right_col_sum:\n",
    "            return 'L'\n",
    "        else:\n",
    "            return 'R'\n",
    "    \n",
    "    # Crop the useless background of the image\n",
    "    def _crop(self, img):\n",
    "        bin_img = self._binarize(img, threshold=5)\n",
    "        contour = self._extract_contour(bin_img)\n",
    "        img = self._erase_background(img, contour)\n",
    "        x1, x2 = np.min(contour[:, :, 0]), np.max(contour[:, :, 0])\n",
    "        y1, y2 = np.min(contour[:, :, 1]), np.max(contour[:, :, 1])\n",
    "        x1, x2 = int(0.99 * x1), int(1.01 * x2)\n",
    "        y1, y2 = int(0.99 * y1), int(1.01 * y2)\n",
    "        return img[y1:y2, x1:x2]\n",
    "    \n",
    "    # Binarize the image at the threshold\n",
    "    def _binarize(self, img, threshold):\n",
    "        return (img > threshold).astype(np.uint8)\n",
    "    \n",
    "    # Get contour points of the breast\n",
    "    def _extract_contour(self, bin_img):\n",
    "        contours, _ = cv2.findContours(\n",
    "            bin_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n",
    "        contour = max(contours, key=cv2.contourArea)\n",
    "        return contour\n",
    "    \n",
    "    # Set to background pixels of the image to zero\n",
    "    def _erase_background(self, img, contour):\n",
    "        mask = np.zeros(img.shape, np.uint8)\n",
    "        cv2.drawContours(mask, [contour], -1, 255, cv2.FILLED)\n",
    "        output = cv2.bitwise_and(img, mask)\n",
    "        return output\n",
    "    \n",
    "    # Resize the image to the preprocessor size\n",
    "    def _resize(self, img):\n",
    "        \n",
    "        #print(img.shape, self.size)\n",
    "        target_size = (self.size[1], self.size[0])\n",
    "        \n",
    "        if (img.shape[1] / img.shape[0]) > (target_size[1] / target_size[0]):\n",
    "            img_resized = image_resize(img, width=target_size[1])\n",
    "            # Pad vertically\n",
    "            pad = target_size[0] - img_resized.shape[0]\n",
    "            img_resized = cv2.copyMakeBorder(img_resized, pad // 2, pad - pad // 2, 0, 0, cv2.BORDER_CONSTANT, value=0)\n",
    "        else:\n",
    "            img_resized = image_resize(img, height=target_size[0])\n",
    "            # Pad horizontally\n",
    "            pad = target_size[1] - img_resized.shape[1]\n",
    "            img_resized = cv2.copyMakeBorder(img_resized, 0, 0, pad // 2, pad - pad // 2, cv2.BORDER_CONSTANT, value=0)\n",
    "\n",
    "        return img_resized\n",
    "    \n",
    "    # Get the save path of a given dicom file\n",
    "    def _get_save_path(self, path, png, save_dir):\n",
    "        patient = path.split('/')[-2]\n",
    "        filename = path.split('/')[-1]\n",
    "        if png:\n",
    "            filename = filename.replace('dcm', 'png')\n",
    "        else:\n",
    "            filename = filename.replace('dcm', 'jpeg')\n",
    "        if save_dir:\n",
    "            save_path = os.path.join(self.save_root, save_dir, patient, filename)\n",
    "        else:\n",
    "            save_path = os.path.join(self.save_root, patient, filename)\n",
    "        return save_path\n",
    "    \n",
    "    # Save the preprocessed image\n",
    "    def _save_image(self, img, path, png, save_dir):\n",
    "        save_path = self._get_save_path(path, png, save_dir)\n",
    "        patient_folder = os.path.split(save_path)[0]\n",
    "        \n",
    "        os.makedirs(patient_folder, exist_ok=True)\n",
    "        cv2.imwrite(save_path, img)"
   ]
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     "text": [
      "Parallel preprocessing done!\n",
      "Time = 2104.782 sec\n"
     ]
    }
   ],
   "source": [
    "mp = MammographyPreprocessor(size=(384, 768))\n",
    "\n",
    "mp.preprocess_all(paths, save=True, save_dir='../preprocessed_mp_aspectratio_768_384', parallel=True, n_jobs=32)"
   ]
  }
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