105 lines (104 with data), 2.6 kB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8e4667b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymskt as mskt\n",
    "from pymskt.mesh import BoneMesh, CartilageMesh\n",
    "import SimpleITK as sitk\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "99154fe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "location_seg = '../data/right_knee_example.nrrd'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a09da648",
   "metadata": {},
   "source": [
    "To create a mesh, we must first pass a segmentation image (simpleitk) to the mesh object. This can be done in multiple ways. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7d2d9e0",
   "metadata": {},
   "source": [
    "- Show all of the ways to assign segmentations & meshes (bone & cartilage) to the BoneMesh object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e84de078",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can pass the location of the seg image directly to the function \n",
    "# right now, this takes only single files (.nrrd, .nii.gz, .dcm (3D))\n",
    "# there is currently an issue opened to allow passing a dicom folder. \n",
    "#   https://github.com/gattia/pymskt/issues/16\n",
    "femur = BoneMesh(path_seg_image = location_seg)\n",
    "\n",
    "# alternatively, we can set or update the seg_image later using an actual SitkImage\n",
    "seg_image = sitk.ReadImage(location_seg)\n",
    "femur.seg_image = seg_image\n",
    "\n",
    "# or we can pass the path directly to the `Mesh.read_seg_image()` function: \n",
    "femur.read_seg_image(location_seg_image)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a4f13a9",
   "metadata": {},
   "source": [
    "- Show details on how to crop bones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5034eac3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.12 64-bit ('imaging': conda)",
   "language": "python",
   "name": "python3812jvsc74a57bd06c87bace85be111efbe28079ae26f740e5c4bf58b006b66c98bd87f126cf4b07"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}