MC-Net/data/Pancreas/Pre-processing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from glob import glob\n",
    "from tqdm import tqdm\n",
    "import h5py\n",
    "import nrrd\n",
    "import nibabel as nib\n",
    "import pandas as pd\n",
    "import xlrd\n",
    "import pdb\n",
    "import SimpleITK as sitk\n",
    "from skimage import transform, measure\n",
    "import os\n",
    "import pydicom\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def load_scan(path):\n",
    "    temp = [pydicom.dcmread(path + f) for f in os.listdir(path)]\n",
    "    slices = [t for t in temp if t.Modality == 'CT']\n",
    "    slices.sort(key = lambda x: int(x.InstanceNumber))\n",
    "    try:\n",
    "        slice_thickness = np.abs(slices[0].ImagePositionPatient[2] - slices[1].ImagePositionPatient[2])\n",
    "    except:\n",
    "        slice_thickness = np.abs(slices[0].SliceLocation - slices[1].SliceLocation)      \n",
    "    for s in slices:\n",
    "        s.SliceThickness = slice_thickness        \n",
    "    return slices\n",
    "\n",
    "def get_pixels_hu(scans):\n",
    "    image = np.stack([s.pixel_array for s in scans]) \n",
    "    image = image.astype(np.float32)\n",
    "    # Convert to Hounsfield units (HU)\n",
    "    intercept = scans[0].RescaleIntercept\n",
    "    slope = scans[0].RescaleSlope\n",
    "    \n",
    "    if slope != 1:\n",
    "        image = slope * image.astype(np.float32)\n",
    "        image = image.astype(np.float32)\n",
    "        \n",
    "    image += np.float32(intercept)\n",
    "    \n",
    "    return np.array(image, dtype=np.float32)\n",
    "\n",
    "listt = glob('./Pancreas-CT/Pancreas-CT/*/*/Pancreas-*/')\n",
    "base_dir = \"./TCIA_pancreas_labels-02-05-2017/TCIA_pancreas_labels-02-05-2017/\"\n",
    "\n",
    "for item in tqdm(listt):\n",
    "    name = str(item)\n",
    "    name_id = name[35:39]\n",
    "    patient_ct = load_scan(name)\n",
    "    imgs_ct = get_pixels_hu(patient_ct)\n",
    "\n",
    "    itk_img = sitk.ReadImage(base_dir +'label'+ name_id + '.nii.gz')\n",
    "    origin = itk_img.GetOrigin()\n",
    "    direction = itk_img.GetDirection()\n",
    "    space = itk_img.GetSpacing()\n",
    "    label = sitk.GetArrayFromImage(itk_img)\n",
    "    \n",
    "\n",
    "    image_gz = sitk.GetImageFromArray(imgs_ct)\n",
    "    image_gz.SetOrigin(origin)\n",
    "    image_gz.SetDirection(direction)\n",
    "    image_gz.SetSpacing(space)\n",
    "    sitk.WriteImage(image_gz, \"./image\"+name_id+\".nii.gz\")\n",
    "#     plt.figure(figsize=(10, 10))\n",
    "#     plt.title('CT Slice_10')\n",
    "#     plt.imshow(imgs_ct[9],cmap='gray')\n",
    "#     plt.axis('off')\n",
    "#     plt.show()"
   ]
  },
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    "ExecuteTime": {
     "start_time": "2021-07-08T07:22:32.650Z"
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     "text": [
      "[512 512 223]\n",
      "[500, 500, 223]\n",
      "[0.9765625 0.9765625 1.       ]\n",
      "[1.0, 1.0, 1.0]\n",
      "(500, 500, 223)\n",
      "(202, 146, 223)\n"
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     "text": [
      "[512 512 216]\n",
      "[480, 480, 216]\n",
      "[0.9375 0.9375 1.    ]\n",
      "[1.0, 1.0, 1.0]\n",
      "(480, 480, 216)\n",
      "(215, 146, 216)\n"
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      "[512 512 224]\n",
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      "[512 512 310]\n",
      "[400, 400, 216]\n",
      "[0.78125    0.78125    0.69999999]\n",
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      "[512 512 310]\n",
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      "[512 512 206]\n",
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     "text": [
      "[512 512 233]\n",
      "[500, 500, 233]\n",
      "[0.9765625 0.9765625 1.       ]\n",
      "[1.0, 1.0, 1.0]\n",
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   "source": [
    "import numpy as np\n",
    "from glob import glob\n",
    "from tqdm import tqdm\n",
    "import h5py\n",
    "import nrrd\n",
    "import nibabel as nib\n",
    "import pandas as pd\n",
    "import xlrd\n",
    "import pdb\n",
    "import SimpleITK as sitk\n",
    "from skimage import transform, measure\n",
    "import os\n",
    "import pydicom\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "output_size =[96,96,96]\n",
    "def ImageResample(sitk_image, new_spacing = [1,1,1], is_label = False):\n",
    "    '''\n",
    "    sitk_image:\n",
    "    new_spacing: x,y,z\n",
    "    is_label: if True, using Interpolator `sitk.sitkNearestNeighbor`\n",
    "    '''\n",
    "    size = np.array(sitk_image.GetSize())\n",
    "    spacing = np.array(sitk_image.GetSpacing())\n",
    "    new_spacing = np.array(new_spacing)\n",
    "    new_size = size * spacing / new_spacing\n",
    "    new_spacing_refine = size * spacing / new_size\n",
    "    new_spacing_refine = [float(s) for s in new_spacing_refine]\n",
    "    new_size = [int(s) for s in new_size]\n",
    "    if not is_label:\n",
    "        print(size)\n",
    "        print(new_size)\n",
    "        print(spacing)\n",
    "        print(new_spacing_refine)\n",
    "    resample = sitk.ResampleImageFilter()\n",
    "    resample.SetOutputDirection(sitk_image.GetDirection())\n",
    "    resample.SetOutputOrigin(sitk_image.GetOrigin())\n",
    "    resample.SetSize(new_size)\n",
    "    resample.SetOutputSpacing(new_spacing_refine)\n",
    "\n",
    "    if is_label:\n",
    "        resample.SetInterpolator(sitk.sitkNearestNeighbor)\n",
    "    else:\n",
    "        resample.SetInterpolator(sitk.sitkBSpline)\n",
    "    newimage = resample.Execute(sitk_image)\n",
    "    return newimage\n",
    "\n",
    "def set_window_wl_ww(tensor):\n",
    "    sl_window = [75,400]\n",
    "    [wl,ww] = sl_window\n",
    "    w_min, w_max = wl - ww//2, wl + ww//2\n",
    "    tensor[tensor < w_min] = w_min\n",
    "    tensor[tensor > w_max] = w_max\n",
    "    tensor = (tensor - w_min) / (w_max - w_min)\n",
    "    ### min max Normalization\n",
    "    return tensor\n",
    "\n",
    "def crop_roi(image, label):\n",
    "    assert(image.shape == label.shape)\n",
    "    print (image.shape)\n",
    "    ### crop based on lung segmentation\n",
    "    w, h, d = label.shape\n",
    "\n",
    "    tempL = np.nonzero(label)\n",
    "    minx, maxx = np.min(tempL[0]), np.max(tempL[0])\n",
    "    miny, maxy = np.min(tempL[1]), np.max(tempL[1])\n",
    "\n",
    "    px = max(output_size[0] - (maxx - minx), 0) // 2\n",
    "    py = max(output_size[1] - (maxy - miny), 0) // 2\n",
    "    minx = max(minx - px - 25, 0) #np.random.randint(5, 10)\n",
    "    maxx = min(maxx + px + 25, w) #np.random.randint(5, 10)\n",
    "    miny = max(miny - py - 25, 0)\n",
    "    maxy = min(maxy + py + 25, h)\n",
    "    \n",
    "    image = image[minx:maxx, miny:maxy,:].astype(np.float32)\n",
    "    label = label[minx:maxx, miny:maxy,:].astype(np.float32)\n",
    "    return image, label\n",
    "\n",
    "listt = glob('./Pancreas/image/*')\n",
    "\n",
    "for item in tqdm(listt):\n",
    "    name_image = str(item)\n",
    "    name_label = name_image.replace('image', 'label')\n",
    "#     pdb.set_trace\n",
    "    itk_img = sitk.ReadImage(name_image)\n",
    "#     origin =itk_img.GetOrigin()\n",
    "#     direction = itk_img.GetDirection()\n",
    "#     space = itk_img.GetSpacing()\n",
    "    itk_img = ImageResample(itk_img)\n",
    "    image = sitk.GetArrayFromImage(itk_img)\n",
    "    image = np.transpose(image, (2,1,0))\n",
    "    \n",
    "    itk_label = sitk.ReadImage(name_label)\n",
    "    itk_label = ImageResample(itk_label, is_label = True)\n",
    "    label = sitk.GetArrayFromImage(itk_label)\n",
    "    label = np.transpose(label, (2,1,0))\n",
    "\n",
    "    assert(np.max(label) == 1 and np.min(label) == 0)\n",
    "    assert(np.unique(label).shape[0] == 2)\n",
    "    assert(np.shape(label)==np.shape(image))\n",
    "    image = set_window_wl_ww(image)\n",
    "#     print(image.shape)\n",
    "#     plt.figure(figsize=(10, 10))\n",
    "#     plt.title('CT Slice_enhanced_100')\n",
    "#     plt.imshow(image[:,:,100],cmap='gray')\n",
    "#     plt.show()\n",
    "    image, label = crop_roi(image, label)\n",
    "    image = (image - np.mean(image)) / np.std(image)\n",
    "    print(image.shape)\n",
    "    f = h5py.File(('./Pancreas_h5/image'+name_image[22:26] + '_norm.h5'), 'w')\n",
    "    f.create_dataset('image', data=image, compression=\"gzip\")\n",
    "    f.create_dataset('label', data=label, compression=\"gzip\")\n",
    "    f.close()"
   ]
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