Datasets:
File size: 6,076 Bytes
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"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "741c0ad2-1f11-4981-b8d5-3f29e4fccde7",
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"from typing import Any, Dict, Tuple, Union\n",
"\n",
"import os\n",
"import numpy as np\n",
"import PIL\n",
"import SimpleITK"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b21ce89f-c656-4137-b27c-8ed352fbd5ed",
"metadata": {},
"outputs": [],
"source": [
"###########################################\n",
"# PARAMETERS TO PLAY WITH\n",
"\n",
"# Select the patient identification (scalar value between 1 and 45)\n",
"patient_id = 1\n",
"time_id = \"ED\" # ED or ES"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "840099b2-2cbb-45fa-909c-042ea9ad0173",
"metadata": {},
"outputs": [],
"source": [
"def sitk_load(filepath: Union[str, Path]) -> Tuple[np.ndarray, Dict[str, Any]]:\n",
" \"\"\"Loads an image using SimpleITK and returns the image and its metadata.\n",
"\n",
" Args:\n",
" filepath: Path to the image.\n",
"\n",
" Returns:\n",
" - ([N], H, W), Image array.\n",
" - Collection of metadata.\n",
" \"\"\"\n",
" # Load image and save info\n",
" image = SimpleITK.ReadImage(str(filepath))\n",
" info = {\"origin\": image.GetOrigin(), \"spacing\": image.GetSpacing(), \"direction\": image.GetDirection()}\n",
"\n",
" # Extract numpy array from the SimpleITK image object\n",
" im_array = np.squeeze(SimpleITK.GetArrayFromImage(image))\n",
"\n",
" return im_array, info\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a4c0c7fb-374e-4b1a-854a-ca02ecd1651a",
"metadata": {},
"outputs": [],
"source": [
"# Specify the ultrasound/segmentation pair to be loaded\n",
"patient_name = f\"patient{patient_id:02d}\"\n",
"patient_dir = Path(f\"../dataset/{patient_name}\")\n",
"path_to_bmode_image = patient_dir / f\"{patient_name}_{time_id}.nii.gz\"\n",
"path_to_gt_segmentation = patient_dir / f\"{patient_name}_{time_id}_gt.nii.gz\"\n",
"\n",
"# Call of a specific function that reads the .nii.gz files and gives access to the corresponding images and metadata\n",
"bmode, info = sitk_load(path_to_bmode_image)\n",
"voxelspacing = info['spacing']\n",
"depth, width, height = bmode.shape\n",
"gt, info_gt = sitk_load(path_to_gt_segmentation)\n",
"voxelspacing_gt = info_gt['spacing']\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cb09f80f-d055-40c0-8847-14d1778db212",
"metadata": {},
"outputs": [],
"source": [
"# Display the corresponding useful information\n",
"print(f\"{type(bmode)=}\")\n",
"print(f\"{bmode.dtype=}\")\n",
"print(f\"{bmode.shape=}\")\n",
"print(f\"{voxelspacing=}\")\n",
"print('')\n",
"\n",
"print(f\"{type(gt)=}\")\n",
"print(f\"{gt.dtype=}\")\n",
"print(f\"{gt.shape=}\")\n",
"print(f\"{voxelspacing_gt=}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b7c9503a-09f3-4fe7-9412-e0d4900a8455",
"metadata": {},
"outputs": [],
"source": [
"# Display one Y-slice\n",
"\n",
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"slice_id = width // 2\n",
"\n",
"px = 1/plt.rcParams['figure.dpi'] # pixel in inches\n",
"fig = plt.figure(figsize=(depth*px*1.5, height*px*1.5))\n",
"bmode_im = plt.imshow(bmode[:,slice_id,:], cmap='gray',vmin=0,vmax=255)\n",
"gt_im = plt.imshow(np.ma.masked_where(gt[:,slice_id,:] == 0, gt[:,slice_id,:]), interpolation='none', cmap='jet', alpha=0.5)\n",
"plt.axis(\"off\")\n",
"plt.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c92aa98e-89dc-485d-84b3-77c47d9bbdae",
"metadata": {},
"outputs": [],
"source": [
"# Display the set of Y-slices as a sequence \n",
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib import animation\n",
"from IPython.display import HTML\n",
"\n",
"px = 1/plt.rcParams['figure.dpi'] # pixel in inches\n",
"fig = plt.figure(figsize=(depth*px*1.5, height*px*1.5))\n",
"bmode_im = plt.imshow(bmode[:,0,:], cmap='gray', vmin=0, vmax=255)\n",
"gt_im = plt.imshow(np.ma.masked_where(gt[:,0,:] == 0, gt[:,0,:]), interpolation='none', cmap='jet', alpha=0.5)\n",
"plt.axis(\"off\")\n",
"plt.tight_layout()\n",
"plt.close() # this is required to not display the generated image\n",
"\n",
"def init():\n",
" \"\"\"Function that initializes the first frame of the video\"\"\"\n",
" bmode_im.set_data(bmode[:,0,:])\n",
" gt_im.set_data(gt[:,0,:])\n",
"\n",
"def animate(frame_idx):\n",
" \"\"\"Callback that fetches the data for subsequent frames.\"\"\"\n",
" bmode_im.set_data(bmode[:,frame_idx,:])\n",
" gt_im.set_data(np.ma.masked_where(gt[:,frame_idx,:] == 0, gt[:,frame_idx,:]))\n",
" return bmode_im, gt_im\n",
"\n",
"interval = 10000 / width # Adjust delay between frames so that animation lasts 10 seconds\n",
"anim = animation.FuncAnimation(fig, animate, init_func=init, frames=len(bmode), interval=interval)\n",
"HTML(anim.to_html5_video())\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ff197f38-1095-4304-bebe-14365bdf0663",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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