135 lines (134 with data), 4.6 kB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d111d29e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymskt as mskt\n",
    "from pymskt.mesh import BoneMesh, CartilageMesh\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "789c7b95",
   "metadata": {},
   "outputs": [],
   "source": [
    "location_seg = '../data/right_knee_example.nrrd'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8d3b74d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gattia/opt/miniconda3/envs/imaging/lib/python3.8/site-packages/pyvista/core/dataset.py:1401: PyvistaDeprecationWarning: Use of `point_arrays` is deprecated. Use `point_data` instead.\n",
      "  warnings.warn(\n",
      "/Users/gattia/opt/miniconda3/envs/imaging/lib/python3.8/site-packages/pyvista/core/dataset.py:1541: PyvistaDeprecationWarning: Use of `cell_arrays` is deprecated. Use `cell_data` instead.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# initiate the bone mesh object\n",
    "femur = BoneMesh(path_seg_image=location_seg,  # path to the segmentation iamge being used\n",
    "                 label_idx=5,                  # what is the label of this bone\n",
    "                 list_cartilage_labels=[1])    # a list of labels for cartialge associated w/ this bone\n",
    "# Create the bone mesh\n",
    "femur.create_mesh(smooth_image_var=1.0)\n",
    "# Resample the bone surface to have a specified number of nodes. \n",
    "femur.resample_surface(clusters=20000)\n",
    "# Calcualte cartialge thickness for the cartialge meshes associated with the bone\n",
    "femur.calc_cartilage_thickness(image_smooth_var_cart=0.5)\n",
    "\n",
    "# initiate the bone mesh object\n",
    "tibia = BoneMesh(path_seg_image=location_seg,  # path to the segmentation iamge being used\n",
    "                 label_idx=6,                  # what is the label of this bone\n",
    "                 list_cartilage_labels=[2,3])    # a list of labels for cartialge associated w/ this bone\n",
    "# Create the bone mesh\n",
    "tibia.create_mesh(smooth_image_var=1.0)\n",
    "# Resample the bone surface to have a specified number of nodes. \n",
    "tibia.resample_surface(clusters=20000)\n",
    "# Calcualte cartialge thickness for the cartialge meshes associated with the bone\n",
    "tibia.calc_cartilage_thickness(image_smooth_var_cart=0.5)\n",
    "\n",
    "# initiate the bone mesh object\n",
    "patella = BoneMesh(path_seg_image=location_seg,  # path to the segmentation iamge being used\n",
    "                   label_idx=7,                  # what is the label of this bone\n",
    "                   list_cartilage_labels=[4])    # a list of labels for cartialge associated w/ this bone\n",
    "# Create the bone mesh\n",
    "patella.create_mesh(smooth_image_var=1.0)\n",
    "# Resample the bone surface to have a specified number of nodes. \n",
    "patella.resample_surface(clusters=20000)\n",
    "# Calcualte cartialge thickness for the cartialge meshes associated with the bone\n",
    "patella.calc_cartilage_thickness(image_smooth_var_cart=0.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c8545c2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "93f0b8b465c242c7baa89c10d50e8a1f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Viewer(geometries=[{'vtkClass': 'vtkPolyData', 'points': {'vtkClass': 'vtkPoints', 'name': '_points', 'numberO…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itkwidgets import view\n",
    "\n",
    "view(geometries=[femur.mesh, tibia.mesh, patella.mesh])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebcd94a9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
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   "pygments_lexer": "ipython3",
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