tutorial_pointregistation.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Point Registration\n",
    "\n",
    "This tutorial shows you how to register 'nii' files with points. The points can be computed by segmentations like in Verse19.\n",
    "\n",
    "You can get the Verse19 data from:\n",
    "\n",
    "https://osf.io/nqjyw/\n",
    "\n",
    "We assume in this tutorial that you copied the \"dataset-verse19validation\" folder next to this file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from pointreg import ridged_from_points, resample\n",
    "from BIDS import Centroids, NII\n",
    "from BIDS.snapshot2D import Snapshot_Frame, create_snapshot\n",
    "from IPython.display import Image"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ct1 = Path(\"dataset-verse19validation/rawdata/sub-verse018/sub-verse018_ct.nii.gz\")\n",
    "# ct2 = Path(\"dataset-verse19validation/rawdata/sub-verse026/sub-verse026_ct.nii.gz\")\n",
    "\n",
    "ct1 = Path(\"dataset-verse19validation/rawdata/sub-verse404/sub-verse404_split-verse256_ct.nii.gz\")\n",
    "ct2 = Path(\"dataset-verse19validation/rawdata/sub-verse404/sub-verse404_split-verse209_ct.nii.gz\")\n",
    "\n",
    "\n",
    "# ct1 = Path(\"dataset-verse19validation/rawdata/sub-verse412/sub-verse412_split-verse290_ct.nii.gz\")\n",
    "# ct2 = Path(\"dataset-verse19validation/rawdata/sub-verse412/sub-verse412_split-verse235_ct.nii.gz\")\n",
    "\n",
    "\n",
    "def to_vert(p: Path):\n",
    "    return Path(str(p).replace(\"rawdata\", \"derivatives\").replace(\"_ct.nii.gz\", \"_seg-vert_msk.nii.gz\"))\n",
    "\n",
    "\n",
    "def to_poi(p: Path):\n",
    "    return Path(str(p).replace(\"rawdata\", \"derivatives\").replace(\"_ct.nii.gz\", \"_seg-vb_ctd.json\"))\n",
    "\n",
    "\n",
    "c1_vert = to_vert(ct1)\n",
    "c2_vert = to_vert(ct2)\n",
    "c1_poi = to_poi(ct1)\n",
    "c2_poi = to_poi(ct2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From Centroid or Points of Interest(POI) list\n",
    "\n",
    "We load the POIs form the json. It should have the following shape:\n",
    "```[\n",
    "    {\n",
    "        \"direction\": [\"P\",\"I\",\"R\"]\n",
    "    },\n",
    "    {\n",
    "        \"label\": 20, \"X\": 91.1,\"Y\": 40.0, \"Z\": 95.0\n",
    "    },\n",
    "    {\n",
    "        \"label\": 21, \"X\": 76.5,\"Y\": 68.1, \"Z\": 90.3\n",
    "    },\n",
    "    {\n",
    "        \"label\": 22, \"X\": 63.6,\"Y\": 101.2, \"Z\": 86.3\n",
    "    },\n",
    "    {\n",
    "        \"label\": 23, \"X\": 59.0,\"Y\": 136.8, \"Z\": 86.8\n",
    "    },\n",
    "    {\n",
    "        \"label\": 24, \"X\": 72.1,\"Y\": 166.6, \"Z\": 94.0\n",
    "    }\n",
    "]\n",
    "```\n",
    "\n",
    "where the coordinate are the local cords of the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n",
      "('P', 'S', 'R')\n",
      "(-0.03768163190368767, 0.014190344115167113, -0.9991890355438093, 0.9988955728122845, -0.02754874534181795, -0.03806180823489409, 0.028066514445031738, 0.9995197350547836, 0.013136590231944465)\n",
      "[*] used centroids: [7 8 9]\u001b[0m\n",
      "[*] key|fixed points           |moved points after     |moved points before    |delta fixed/moved      |distF|distM|\u001b[0m\n",
      "[*] 7  |   20.9, -226.6, -441.5|   20.9, -226.5, -441.5|   19.5, -233.1, -350.9|    0.0,   -0.1,   -0.0|     |     |\u001b[0m\n",
      "[*] 8  |   22.2, -219.4, -460.7|   22.3, -219.5, -460.7|   21.4, -225.0, -369.7|   -0.1,    0.1,    0.0|20.5 |20.6 |\u001b[0m\n",
      "[*] 9  |   24.4, -212.6, -482.2|   24.4, -212.5, -482.1|   24.4, -216.5, -390.4|    0.0,   -0.1,   -0.1|22.7 |22.6 |\u001b[0m\n",
      "[*] Error avg registration error-vector length:   0.128\u001b[0m\n",
      "[*] Error avg point-distances:   0.100\u001b[0m\n",
      "[ ] 'set_array' with different dtype: from float32 to uint8\u001b[0m\n",
      "[ ] 'set_array' with different dtype: from float32 to uint8\u001b[0m\n",
      "[ ] 'set_array' with different dtype: from float32 to uint8\u001b[0m\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Given centroid file in local space an nii we can register two images\n",
    "resampler, resampler_seg, transform = ridged_from_points(\n",
    "    Centroids().load(c1_poi), Centroids().load(c2_poi), representative_fixed=ct1, representative_movig=ct2\n",
    ")\n",
    "assert resampler is not None\n",
    "assert resampler_seg is not None\n",
    "assert transform is not None\n",
    "rep_moving_nii: NII = resample(resampler, resampler_seg, moving_img=ct2)\n",
    "rep_seg_moving_nii: NII = resample(resampler, resampler_seg, moving_img=c2_vert, is_seg=True)\n",
    "ct1_frame = Snapshot_Frame(ct1, segmentation=c1_vert, centroids=c1_poi, mode=\"CT\")\n",
    "ct2_frame = Snapshot_Frame(rep_moving_nii, segmentation=rep_seg_moving_nii, centroids=c1_poi, mode=\"CT\")\n",
    "\n",
    "c2_poi_trans = transform.transform_points(Centroids().load(c2_poi), origins_shift2=None)\n",
    "ct3_frame = Snapshot_Frame(rep_moving_nii, segmentation=rep_seg_moving_nii, centroids=c2_poi_trans, mode=\"CT\")\n",
    "create_snapshot(\"test.jpg\", [ct1_frame, ct2_frame, ct3_frame])\n",
    "Image(filename=\"test.jpg\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The moved images has some space unoccupied. We can remove non corresponding spaces by cropping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ] 'set_array' with different dtype: from float32 to uint8\u001b[0m\n",
      "index 217 is out of bounds for axis 0 with size 106\n",
      "index 217 is out of bounds for axis 0 with size 106\n",
      "[ ] 'set_array' with different dtype: from float32 to uint8\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from BIDS import to_nii, NII\n",
    "\n",
    "# NII is a wrapper around nibabel\n",
    "ct1_nii = to_nii(ct1).copy()\n",
    "ct2_nii = rep_moving_nii.copy()\n",
    "ct1_seg = to_nii(c1_vert, seg=True)\n",
    "ct2_seg = rep_seg_moving_nii.copy()\n",
    "\n",
    "ex_slice_f = ct1_nii.compute_crop_slice()\n",
    "shared_pixels = ct2_nii.compute_crop_slice(other_crop=ex_slice_f)\n",
    "\n",
    "\n",
    "c1_poi_cropped = Centroids().load(c1_poi).shift_all_centroid_coordinates(shared_pixels)\n",
    "ct1_nii.apply_crop_slice_(shared_pixels)\n",
    "ct2_nii.apply_crop_slice_(shared_pixels)\n",
    "ct1_seg.apply_crop_slice_(shared_pixels)\n",
    "ct2_seg.apply_crop_slice_(shared_pixels)\n",
    "c2_poi_trans2 = c2_poi_trans.shift_all_centroid_coordinates(shared_pixels)\n",
    "\n",
    "ct1_frame = Snapshot_Frame(ct1_nii, segmentation=ct1_seg, centroids=c1_poi_cropped, mode=\"CT\")\n",
    "ct2_frame = Snapshot_Frame(ct2_nii, segmentation=ct2_seg, centroids=c2_poi_trans2, mode=\"CT\")\n",
    "create_snapshot(\"test.jpg\", [ct1_frame, ct2_frame])\n",
    "Image(filename=\"test.jpg\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing Centroids from segmentation\n",
    "\n",
    "We can generate Centroids/POI by computing the Center Of Mass of Segmentation.\n",
    "\n",
    "Variant 1: Just using the segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shp=(218, 667, 86); ori=('P', 'S', 'R'), zoom=(1.0, 1.0, 1.9999613), seg=False\n",
      "orientation ('P', 'S', 'R')\n",
      "shape (218, 667, 86)\n",
      "zoom (1.0, 1.0, 1.999961256980896)\n",
      "points dict_keys([7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24])\n",
      "id 9 (127.0, 604.0, 41.0)\n",
      "\n",
      " Centroids(orientation=('P', 'S', 'R'), centroids={7: (112.0, 644.0, 42.0), 8: (120.0, 626.0, 41.0), 9: (127.0, 604.0, 41.0), 10: (134.0, 581.0, 39.0), 11: (139.0, 557.0, 39.0), 12: (143.0, 533.0, 39.0), 13: (145.0, 508.0, 40.0), 14: (142.0, 483.0, 41.0), 15: (138.0, 458.0, 41.0), 16: (132.0, 432.0, 41.0), 17: (126.0, 406.0, 42.0), 18: (117.0, 378.0, 42.0), 19: (108.0, 349.0, 41.0), 20: (102.0, 316.0, 40.0), 21: (95.0, 281.0, 38.0), 22: (90.0, 247.0, 37.0), 23: (89.0, 214.0, 38.0), 24: (95.0, 181.0, 41.0)}, location=<Location.Unknown: 0>, zoom=(1.0, 1.0, 1.999961256980896), info={})\n"
     ]
    }
   ],
   "source": [
    "from BIDS import calc_centroids\n",
    "\n",
    "ctd = calc_centroids(c1_vert, decimals=0)\n",
    "print(to_nii(c1_vert))\n",
    "print(\"orientation\", ctd.orientation)\n",
    "print(\"shape\", ctd.shape)\n",
    "print(\"zoom\", ctd.zoom)\n",
    "print(\"points\", ctd.keys())\n",
    "print(\"id 9\", ctd[9])\n",
    "print(\"\\n\", ctd)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variant 2: Segmentation and Subregion Segmentation\n",
    "\n",
    "If you have two files, where the second splits the other in smaller chunks you can use this variant. (Verse does not provide this...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ] 'set_array' with different dtype: from uint8 to uint16\u001b[0m\n",
      "Centroids(orientation=('P', 'S', 'R'), centroids={12807: (112.2, 643.9, 41.7), 12808: (119.8, 625.5, 41.4), 12809: (126.7, 603.8, 40.6), 12810: (134.3, 580.7, 39.4), 12811: (139.4, 557.2, 39.0), 12812: (143.2, 533.2, 39.3), 12813: (144.6, 508.0, 40.2), 12814: (142.2, 482.7, 40.9), 12815: (137.6, 457.6, 41.3), 12816: (132.1, 432.1, 41.4), 12817: (126.0, 406.3, 41.9), 12818: (116.8, 378.0, 41.7), 12819: (107.8, 348.6, 41.1), 12820: (101.8, 316.2, 40.1), 12821: (95.4, 281.0, 38.3), 12822: (90.2, 247.4, 37.4), 12823: (88.8, 214.0, 38.4), 12824: (95.0, 181.3, 41.1)}, location=<Location.Multi: 256>, zoom=(1.0, 1.0, 1.999961256980896), info={})\n",
      "[(50, 7, (112.2, 643.9, 41.7)), (50, 8, (119.8, 625.5, 41.4)), (50, 9, (126.7, 603.8, 40.6)), (50, 10, (134.3, 580.7, 39.4)), (50, 11, (139.4, 557.2, 39.0)), (50, 12, (143.2, 533.2, 39.3)), (50, 13, (144.6, 508.0, 40.2)), (50, 14, (142.2, 482.7, 40.9)), (50, 15, (137.6, 457.6, 41.3)), (50, 16, (132.1, 432.1, 41.4)), (50, 17, (126.0, 406.3, 41.9)), (50, 18, (116.8, 378.0, 41.7)), (50, 19, (107.8, 348.6, 41.1)), (50, 20, (101.8, 316.2, 40.1)), (50, 21, (95.4, 281.0, 38.3)), (50, 22, (90.2, 247.4, 37.4)), (50, 23, (88.8, 214.0, 38.4)), (50, 24, (95.0, 181.3, 41.1))]\n",
      "Note: the ids are subregion_ID*256 + segmentation id. Matching points MUST have the same ID\n"
     ]
    }
   ],
   "source": [
    "vert_nii = to_nii(c1_vert, seg=True)\n",
    "arr = vert_nii.get_array()\n",
    "arr[arr != 0] = 50\n",
    "subregion_nii = vert_nii.set_array(arr, inplace=False)\n",
    "from BIDS import calc_centroids_from_subreg_vert\n",
    "\n",
    "ctd = calc_centroids_from_subreg_vert(vert_nii, subregion_nii, subreg_id=[50])\n",
    "print(ctd)\n",
    "print(list(ctd.items2()))\n",
    "print(\"Note: the ids are subregion_ID*256 + segmentation id. Matching points MUST have the same ID\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use a function that does all at once and use the Vertebra center and the mask centers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys([7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280])\n",
      "dict_keys([2, 3, 4, 5, 6, 7, 8, 9, 257, 258, 259, 260, 261, 262, 263, 264, 265])\n",
      "[*] Register to shp=(218, 667, 86); ori=('P', 'S', 'R'), zoom=(1.0, 1.0, 1.9999613), seg=False\u001b[0m\n",
      "[7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280]\n",
      "('P', 'S', 'R')\n",
      "(-0.03768163190368767, 0.014190344115167113, -0.9991890355438093, 0.9988955728122845, -0.02754874534181795, -0.03806180823489409, 0.028066514445031738, 0.9995197350547836, 0.013136590231944465)\n",
      "[*] used centroids: [  7   8   9 263 264 265]\u001b[0m\n",
      "[*] key|fixed points           |moved points after     |moved points before    |delta fixed/moved      |distF|distM|\u001b[0m\n",
      "[*] 7  |   20.9, -226.6, -441.5|   21.1, -226.5, -441.4|   19.5, -233.1, -350.9|   -0.2,   -0.1,   -0.1|     |     |\u001b[0m\n",
      "[*] 8  |   22.2, -219.4, -460.7|   22.2, -219.6, -460.7|   21.4, -225.0, -369.7|    0.0,    0.2,   -0.0|20.5 |20.6 |\u001b[0m\n",
      "[*] 9  |   24.4, -212.6, -482.2|   24.3, -212.5, -482.0|   24.4, -216.5, -390.4|    0.1,   -0.1,   -0.2|22.7 |22.6 |\u001b[0m\n",
      "[*] 263|   21.4, -215.5, -438.1|   21.3, -215.4, -438.3|   19.2, -222.2, -347.2|    0.1,   -0.1,    0.2|     |     |\u001b[0m\n",
      "[*] 264|   22.3, -207.9, -456.4|   22.3, -207.9, -456.5|   20.9, -213.6, -364.8|    0.0,    0.0,    0.1|19.8 |19.7 |\u001b[0m\n",
      "[*] 265|   24.2, -201.1, -478.2|   24.3, -201.0, -478.0|   23.9, -205.3, -385.7|   -0.1,   -0.1,   -0.2|22.9 |22.7 |\u001b[0m\n",
      "[*] Error avg registration error-vector length:   0.213\u001b[0m\n",
      "[*] Error avg point-distances:   0.125\u001b[0m\n",
      "[*] Resample Moving to Target\u001b[0m\n",
      "[ ] 'set_array' with different dtype: from float32 to uint8\u001b[0m\n",
      "index 217 is out of bounds for axis 0 with size 95\n",
      "index 217 is out of bounds for axis 0 with size 95\n",
      "[ ] 'set_array' with different dtype: from float32 to uint8\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pointreg import ridged_registration_cdt\n",
    "\n",
    "poi1 = Centroids().load(to_poi(ct1))\n",
    "poi1_2 = calc_centroids(to_nii(c1_vert, True))\n",
    "for k, v in poi1_2.items():\n",
    "    poi1[k + 256] = v\n",
    "print(poi1.keys())\n",
    "\n",
    "poi2 = Centroids().load(to_poi(ct2))\n",
    "poi2_2 = calc_centroids(to_nii(c2_vert, True))\n",
    "for k, v in poi2_2.items():\n",
    "    poi2[k + 256] = v\n",
    "print(poi2.keys())\n",
    "fixed, moving, (_, _, point_trans, crop) = ridged_registration_cdt(\n",
    "    ctd_fixed=poi1,\n",
    "    ctd_movig=poi2,\n",
    "    list_fixed=[to_nii(ct1, False), to_nii(c1_vert, True)],\n",
    "    list_moving=[to_nii(ct2, False), to_nii(c2_vert, True)],\n",
    ")\n",
    "poi1_reg = poi1.crop_centroids(crop)\n",
    "poi2_reg = point_trans.transform_points(poi2, crop)\n",
    "ct1_frame = Snapshot_Frame(fixed[0], segmentation=fixed[1], centroids=poi1_reg, mode=\"CT\")\n",
    "ct2_frame = Snapshot_Frame(moving[0], segmentation=moving[1], centroids=poi2_reg, mode=\"CT\")\n",
    "create_snapshot(\"test.jpg\", [ct1_frame, ct2_frame])\n",
    "Image(filename=\"test.jpg\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see that the lower vertebras have their spinous process visible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ] 'set_array' with different dtype: from float32 to uint8\u001b[0m\n",
      "index 217 is out of bounds for axis 0 with size 95\n",
      "index 217 is out of bounds for axis 0 with size 95\n",
      "[ ] 'set_array' with different dtype: from float32 to uint8\u001b[0m\n",
      "[ ] 'set_array' with different dtype: from float32 to uint8\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct1_frame = Snapshot_Frame(\n",
    "    fixed[0],\n",
    "    segmentation=fixed[1],\n",
    "    centroids=poi1_reg,\n",
    "    mode=\"CT\",\n",
    ")\n",
    "ct2_frame = Snapshot_Frame(\n",
    "    moving[0],\n",
    "    segmentation=moving[1],\n",
    "    centroids=poi2_reg,\n",
    "    mode=\"CT\",\n",
    ")\n",
    "\n",
    "ct2_frame_single = Snapshot_Frame(ct2_nii, segmentation=ct2_seg, centroids=c2_poi_trans2, mode=\"CT\")\n",
    "create_snapshot(\"test.jpg\", [ct1_frame, ct2_frame, ct2_frame_single])\n",
    "Image(filename=\"test.jpg\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Locking at the coronal direction you should see that the vertebra are rotated unlike the other image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ] 'set_array' with different dtype: from float32 to uint8\u001b[0m\n",
      "index 217 is out of bounds for axis 0 with size 95\n",
      "index 217 is out of bounds for axis 0 with size 95\n",
      "[ ] 'set_array' with different dtype: from float32 to uint8\u001b[0m\n",
      "[ ] 'set_array' with different dtype: from float32 to uint8\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct1_frame = Snapshot_Frame(fixed[0], segmentation=fixed[1], centroids=poi1_reg, mode=\"CT\", coronal=True, sagittal=False)\n",
    "ct2_frame = Snapshot_Frame(moving[0], segmentation=moving[1], centroids=poi2_reg, mode=\"CT\", coronal=True, sagittal=False)\n",
    "c2_poi_ = c2_poi_trans2\n",
    "for k, v in calc_centroids(to_nii(ct2_seg, True)).items():\n",
    "    c2_poi_[k + 256] = v\n",
    "\n",
    "ct2_frame_single = Snapshot_Frame(ct2_nii, segmentation=ct2_seg, centroids=c2_poi_, mode=\"CT\", coronal=True, sagittal=False)\n",
    "create_snapshot(\"test.jpg\", [ct1_frame, ct2_frame, ct2_frame_single])\n",
    "Image(filename=\"test.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py3.10",
   "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": "3.10.11"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "856182c64741417959fd46a52606ac399fe54e69ad9697a20c4f35644f875db1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}