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{
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
"version": "0.2.3",
"changelog": {
"0.2.3": "enhance metadata with improved descriptions",
"0.2.2": "update to huggingface hosting",
"0.2.1": "fix pytype error",
"0.2.0": "set image_only to False",
"0.1.0": "complete the model package"
},
"monai_version": "1.3.0",
"pytorch_version": "1.13.1",
"numpy_version": "1.24.3",
"optional_packages_version": {
"nibabel": "5.1.0",
"pytorch-ignite": "0.4.11",
"einops": "0.6.1",
"fire": "0.5.0",
"torchvision": "0.14.1",
"tensorboard": "2.17.0",
"scipy": "1.13.1"
},
"name": "Renal Structures CECT Segmentation",
"task": "Multi-class Segmentation of Renal Structures in Contrast-Enhanced CT",
"description": "A 3D UNet-based segmentation model for comprehensive renal structure analysis in contrast-enhanced CT scans. The model processes 96x96x96 voxel patches and identifies six anatomical structures: arteries, veins, ureters, parenchyma, cysts, and tumors.",
"authors": "Sechenov university",
"copyright": "Copyright (c) Sechenov university",
"data_source": "AVUCTK_cases.zip",
"data_type": "nibabel",
"image_classes": "three channel data, intensity scaled to [0, 1]",
"label_classes": "1: artery, 2: vein, 3: ureter, 4: cyst, 5: tumor, 6: parenchyma",
"pred_classes": "1: artery, 2: vein, 3: ureter, 4: neoplasm, 5: parenchyma",
"eval_metrics": {
"mean_dice": 0.79
},
"intended_use": "This is PoC, not to be used for diagnostic purposes",
"references": [
"Chernenkiy I. M. et al. Segmentation of renal structures based on contrast computed tomography scans using a convolutional neural network //Sechenov Medical Journal. \u2013 2023. \u2013 \u0422. 14. \u2013 \u2116. 1. \u2013 \u0421. 39-49. URL - https://www.sechenovmedj.com/jour/article/view/899"
],
"network_data_format": {
"inputs": {
"image": {
"type": "image",
"format": "hounsfield",
"modality": "CT",
"num_channels": 3,
"spatial_shape": [
96,
96,
96
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": true,
"channel_def": {
"0": "image"
}
}
},
"outputs": {
"pred": {
"type": "image",
"format": "segmentation",
"num_channels": 6,
"spatial_shape": [
96,
96,
96
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": true,
"channel_def": {
"0": "background",
"1": "artery",
"2": "vein",
"3": "ureter",
"4": "neoplasm",
"5": "parenchyma"
}
}
}
}
}
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