Segmentation image distance

Segmentation_Image_Distance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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   "source": [
    "from itkwidgets import view, cm\n",
    "import itk\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ground_truth = itk.imread('ground-truth.nii.gz')\n",
    "segmentation = itk.imread('segmentation.nii.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "50cdb3876b184ffeb35ef45205204807",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Viewer(geometries=[], gradient_opacity=0.22, interpolation=False, point_sets=[], rendered_label_image=<itk.itk…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "view(label_image=ground_truth, ui_collapsed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "35512f7950534acf9fb9a805e02c6018",
       "version_major": 2,
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      },
      "text/plain": [
       "Viewer(geometries=[], gradient_opacity=0.22, interpolation=False, point_sets=[], rendered_label_image=<itk.itk…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "view(label_image=segmentation, ui_collapsed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cast to signed integer because we will subtract to obtain negative values\n",
    "ground_truth_signed = np.asarray(ground_truth).astype(np.int16)\n",
    "segmentation_signed = np.asarray(segmentation).astype(np.int16)\n",
    "\n",
    "subtracted = itk.subtract_image_filter(ground_truth_signed, segmentation_signed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "false_positives = itk.binary_threshold_image_filter(subtracted, lower_threshold=1, inside_value=1)\n",
    "false_negatives = itk.binary_threshold_image_filter(subtracted, upper_threshold=-1, inside_value=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "distance = itk.signed_maurer_distance_map_image_filter(false_positives,\n",
    "                                                       inside_is_positive=True)\n",
    "false_positive_distances = itk.mask_image_filter(distance, false_positives)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "distance = itk.signed_maurer_distance_map_image_filter(false_negatives,\n",
    "                                                       inside_is_positive=True)\n",
    "false_negative_distances = itk.mask_image_filter(distance, false_negatives)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7a6d707c4fab4e708d4562063ddcb5bc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Viewer(cmap=['rainbow'], geometries=[], gradient_opacity=0.22, point_sets=[], rendered_image=<itk.itkImagePyth…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "signed_distances = np.asarray(false_positive_distances) + -1 * np.asarray(false_negative_distances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e49d88bfed3a43d6a609b203b298a5d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Viewer(cmap=['rainbow'], geometries=[], gradient_opacity=0.22, opacity_gaussians=[[{'position': 0.769444444444…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "opacity_gaussians = [[{'position': 0.7694444444444445,\n",
    "   'height': 1,\n",
    "   'width': 0.22777777777777775,\n",
    "   'xBias': 0.5127777777777779,\n",
    "   'yBias': 0.5090909090909093},\n",
    "  {'position': 0.21944444444444444,\n",
    "   'height': 1,\n",
    "   'width': 0.2277777777777778,\n",
    "   'xBias': -0.30277777777777776,\n",
    "   'yBias': 0.18181818181818166}]]\n",
    "\n",
    "viewer = view(signed_distances,\n",
    "              cmap=cm.rainbow,\n",
    "              opacity_gaussians=opacity_gaussians,\n",
    "              ui_collapsed=True)\n",
    "viewer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[{'position': 0.7694444444444445,\n",
       "   'height': 1,\n",
       "   'width': 0.22777777777777775,\n",
       "   'xBias': 0.5127777777777779,\n",
       "   'yBias': 0.5090909090909093},\n",
       "  {'position': 0.21944444444444444,\n",
       "   'height': 1,\n",
       "   'width': 0.2277777777777778,\n",
       "   'xBias': -0.30277777777777776,\n",
       "   'yBias': 0.18181818181818166}]]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viewer.opacity_gaussians"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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