353 lines (352 with data), 117.8 kB

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Post-Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import nibabel as nib\n",
    "from scipy import ndimage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pixel Space:  [0.9375, 0.9375]\n",
      "Slice Thickness:  3\n",
      "Volume of Single Voxel =  2.63671875\n"
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "# Voxel Volume\n",
    "\n",
    "import pydicom\n",
    "mri_slice = pydicom.dcmread(\"D:/MRI - Tairawhiti (User POV)/Raw DICOM MRI Scans/7_AutoBindWATER_450_10B/IM-0118-0009.dcm\")\n",
    "\n",
    "pixel_spacing = mri_slice.PixelSpacing\n",
    "slice_thickness = mri_slice.SliceThickness\n",
    "\n",
    "voxel_volume = float(pixel_spacing[0]) * float(pixel_spacing[1]) * float(slice_thickness)\n",
    "print('Pixel Space: ',pixel_spacing)\n",
    "print('Slice Thickness: ', slice_thickness)\n",
    "print('Volume of Single Voxel = ', voxel_volume)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def post_process_segmentation(segmented_image, min_region_size_ratio=0.01):\n",
    "    # Hole Filling\n",
    "    # Find the complement of the segmented image (invert it)\n",
    "    inverted_image = 1 - segmented_image\n",
    "    \n",
    "    # Label connected components in the inverted image\n",
    "    labeled_components, num_components = ndimage.label(inverted_image)\n",
    "    \n",
    "    # Calculate the threshold for removing small holes based on the total segmentation area\n",
    "    total_wound_pixels = np.sum(segmented_image)\n",
    "    min_region_size = int(min_region_size_ratio * total_wound_pixels)\n",
    "    \n",
    "    # Remove small holes by setting them back to 1 (region of interest)\n",
    "    for component in range(1, num_components + 1):\n",
    "        component_size = np.sum(labeled_components == component)\n",
    "        if component_size < min_region_size:\n",
    "            segmented_image[labeled_components == component] = 1\n",
    "\n",
    "    # Step 2: Remove small noise\n",
    "    # Label connected components in the segmented image\n",
    "    labeled_components, num_components = ndimage.label(segmented_image)\n",
    "    \n",
    "    # Calculate the threshold for removing small noise based on the total wound area\n",
    "    min_noise_size = int(min_region_size_ratio * total_wound_pixels)\n",
    "    \n",
    "    # Remove small noise by setting them back to 0 (non-wound)\n",
    "    for component in range(1, num_components + 1):\n",
    "        component_size = np.sum(labeled_components == component)\n",
    "        if component_size < min_noise_size:\n",
    "            segmented_image[labeled_components == component] = 0\n",
    "\n",
    "    return segmented_image\n",
    "\n",
    "\n",
    "def superimpose_images(image1, image2):\n",
    "    image1 = image1 / np.max(image1)\n",
    "    image2 = image2 / np.max(image2)\n",
    "    alpha = 0.5\n",
    "    superimposed_image = alpha * image1 + (1 - alpha) * image2\n",
    "    return superimposed_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import ndimage\n",
    "\n",
    "def denoising_algo(segmented_volume, min_region_size_ratio=0.01):\n",
    "    depth, height, width = segmented_volume.shape\n",
    "    \n",
    "    post_processed_volume = np.zeros_like(segmented_volume)\n",
    "    \n",
    "    for z in range(depth):\n",
    "        segmented_image = segmented_volume[z, :, :]\n",
    "        \n",
    "        # Hole Filling\n",
    "        inverted_image = 1 - segmented_image\n",
    "        labeled_components, num_components = ndimage.label(inverted_image)\n",
    "        total_aoi_pixels = np.sum(segmented_image)\n",
    "        min_region_size = int(min_region_size_ratio * total_aoi_pixels)\n",
    "        \n",
    "        for component in range(1, num_components + 1):\n",
    "            component_size = np.sum(labeled_components == component)\n",
    "            if component_size < min_region_size:\n",
    "                segmented_image[labeled_components == component] = 1\n",
    "\n",
    "        # Remove small noise using adaptive thresholding (small artifact removal)\n",
    "        labeled_components, num_components = ndimage.label(segmented_image)\n",
    "        min_noise_size = int(min_region_size_ratio * total_aoi_pixels)\n",
    "        \n",
    "        for component in range(1, num_components + 1):\n",
    "            component_size = np.sum(labeled_components == component)\n",
    "            if component_size < min_noise_size:\n",
    "                segmented_image[labeled_components == component] = 0\n",
    "        \n",
    "        post_processed_volume[z, :, :] = segmented_image\n",
    "\n",
    "    return post_processed_volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (501,512,512,1) (501,256,256,1) ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[7], line 15\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[39m# msk_pred_data = np.expand_dims(msk_pred_data, axis = -1)\u001b[39;00m\n\u001b[0;32m     13\u001b[0m msk_gt_data \u001b[39m=\u001b[39m msk_gt\u001b[39m.\u001b[39mget_fdata()\n\u001b[1;32m---> 15\u001b[0m \u001b[39msuper\u001b[39m \u001b[39m=\u001b[39m superimpose_images(scan_data, msk_pred_data)\n\u001b[0;32m     17\u001b[0m plt\u001b[39m.\u001b[39mimshow(scan_data[\u001b[39mslice\u001b[39m, :, :, \u001b[39m0\u001b[39m], cmap\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mgray\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m     18\u001b[0m plt\u001b[39m.\u001b[39maxis(\u001b[39m'\u001b[39m\u001b[39moff\u001b[39m\u001b[39m'\u001b[39m)\n",
      "Cell \u001b[1;32mIn[6], line 39\u001b[0m, in \u001b[0;36msuperimpose_images\u001b[1;34m(image1, image2)\u001b[0m\n\u001b[0;32m     37\u001b[0m image2 \u001b[39m=\u001b[39m image2 \u001b[39m/\u001b[39m np\u001b[39m.\u001b[39mmax(image2)\n\u001b[0;32m     38\u001b[0m alpha \u001b[39m=\u001b[39m \u001b[39m0.5\u001b[39m\n\u001b[1;32m---> 39\u001b[0m superimposed_image \u001b[39m=\u001b[39m alpha \u001b[39m*\u001b[39;49m image1 \u001b[39m+\u001b[39;49m (\u001b[39m1\u001b[39;49m \u001b[39m-\u001b[39;49m alpha) \u001b[39m*\u001b[39;49m image2\n\u001b[0;32m     40\u001b[0m \u001b[39mreturn\u001b[39;00m superimposed_image\n",
      "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (501,512,512,1) (501,256,256,1) "
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "fname = 'msk_021'\n",
    "model = 'resnet(0.91 DSC, 3 Patients, 3 Epoches)'\n",
    "base_dir = 'D:/MRI - Tairawhiti (User POV)'\n",
    "slice = 80\n",
    "\n",
    "scan = nib.load(('{}/nnUNet Data/scans/{}.nii.gz').format(base_dir, fname))\n",
    "msk_pred = nib.load(('{}/Pre-Trained Models (Google Colab)/{}/{}.nii.gz').format(base_dir, model, fname))\n",
    "msk_gt = nib.load(('{}/nnUNet Data/multiclass_masks/{}.nii.gz').format(base_dir, fname))\n",
    "\n",
    "scan_data = scan.get_fdata()\n",
    "msk_pred_data = msk_pred.get_fdata()\n",
    "# msk_pred_data = np.expand_dims(msk_pred_data, axis = -1)\n",
    "msk_gt_data = msk_gt.get_fdata()\n",
    "\n",
    "\n",
    "plt.imshow(msk_pred_data[slice, :, :, 0], cmap='gray')\n",
    "plt.axis('off')\n",
    "plt.title('pred mask')\n",
    "plt.show()\n",
    "\n",
    "plt.imshow(msk_gt_data[slice, :, :, 0], cmap='gray')\n",
    "plt.axis('off')\n",
    "plt.title('gt mask')\n",
    "plt.show()\n",
    "\n",
    "print(msk_pred_data.shape)\n",
    "print(msk_gt_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "segmented_image = post_process_segmentation(msk_pred_data[80,:,:,0], min_region_size_ratio=0.01)\n",
    "\n",
    "plt.imshow(segmented_image, cmap='gray')\n",
    "plt.axis('off')\n",
    "plt.title('post-processed mask')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "segmented_image = post_process_segmentation(msk_pred_data[400,:,:,0], min_region_size_ratio=0.01)\n",
    "\n",
    "plt.imshow(segmented_image, cmap='gray')\n",
    "plt.axis('off')\n",
    "plt.title('post-processed mask')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cropping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cropped Prediction Shape:  (833, 256, 176, 1)\n",
      "Cropped Groundtruth Shape:  (833, 256, 176, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from preprocessing import superimpose_images\n",
    "from image_preprocessing import image_cropping\n",
    "\n",
    "slice = 50\n",
    "\n",
    "msk_pred_data_cropped = image_cropping(msk_pred_data, top = 0, bottom = 0, left = 80, right = 0)\n",
    "msk_gt_data_cropped = image_cropping(msk_gt_data, top = 0, bottom = 0, left = 80, right = 0)\n",
    "\n",
    "print('Cropped Prediction Shape: ', msk_pred_data_cropped.shape)\n",
    "print('Cropped Groundtruth Shape: ', msk_gt_data_cropped.shape)\n",
    "\n",
    "plt.imshow(msk_pred_data_cropped[slice, :, :, 0], cmap='gray')\n",
    "plt.axis('off')\n",
    "plt.title('pred mask cropped')\n",
    "plt.show()\n",
    "\n",
    "plt.imshow(msk_gt_data_cropped[slice, :, :, 0], cmap='gray')\n",
    "plt.axis('off')\n",
    "plt.title('gt mask cropped')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "nii_img_pred = nib.Nifti1Image(msk_pred_data_cropped, affine=msk_pred.affine)\n",
    "nii_img_gt = nib.Nifti1Image(msk_gt_data_cropped, affine=msk_gt.affine)\n",
    "\n",
    "if False:\n",
    "    nib.save(nii_img_pred, ('{}/Pre-Trained Models (Google Colab)/{}/{}_pred_postprocessed.nii.gz').format(base_dir, model, fname))\n",
    "    nib.save(nii_img_gt, ('{}/Pre-Trained Models (Google Colab)/{}/{}_gt_postprocessed.nii.gz').format(base_dir, model, fname))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Py39-CNN",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.17"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}