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{
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
"version": "0.0.6",
"changelog": {
"0.0.6": "enhance metadata with improved descriptions",
"0.0.5": "update to huggingface hosting",
"0.0.4": "Set image_only to False",
"0.0.3": "Update for stable MONAI version",
"0.0.2": "Retrain with new MONAI",
"0.0.1": "initialize the model package structure"
},
"monai_version": "1.3.0",
"pytorch_version": "1.13.1",
"numpy_version": "1.22.2",
"required_packages_version": {
"fire": "0.5.0",
"nibabel": "5.1.0",
"pytorch-ignite": "0.4.11",
"pyyaml": "6.0.2"
},
"supported_apps": {},
"name": "Multi-organ Abdominal Segmentation",
"task": "Multi-organ Segmentation in Abdominal CT Images",
"description": "A 3D segmentation model optimized through Neural Architecture Search (DiNTS) that processes 96x96x96 pixel patches from CT scans to segment eight abdominal organs and structures. The model achieves a mean Dice score of 0.88 across all structures, including liver, spleen, pancreas, stomach, gallbladder, and vascular structures (artery and portal vein).",
"authors": "Chen Shen, Holger R. Roth, Kazunari Misawa, Kensaku Mori",
"copyright": "",
"data_source": "Aichi Cancer Center, Japan",
"data_type": "nibabel",
"image_classes": "single channel data, intensity scaled to [0, 1]",
"label_classes": "eight channels data, 1 is artery, 2 is portal vein, 3 is liver, 4 is spleen, 5 is stomach, 6 is gallbladder, 7 is pancreas, 0 is everything else",
"pred_classes": "8 channels OneHot data, 1 is artery, 2 is portal vein, 3 is liver, 4 is spleen, 5 is stomach, 6 is gallbladder, 7 is pancreas, 0 is background",
"eval_metrics": {
"mean_dice": 0.88
},
"intended_use": "This is an example, not to be used for diagnostic purposes",
"references": [
"He, Y., Yang, D., Roth, H., Zhao, C. and Xu, D., 2021. Dints: Differentiable neural network topology search for 3d medical image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5841-5850).",
"Roth, H., Shen C, Oda H., Sugino T., Oda M., Hayashi Y., Misawa K., Mori K., 2018. A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation. International conference on medical image computing and computer-assisted intervention",
"Shen, C., Roth, H. R., Nath, V., Hayashi, Y., Oda, M., Misawa, K., Mori, K., 2022. Effective hyperparameter optimization with proxy data for multi-organ segmentation. In Medical Imaging 2022: Image Processing (Vol. 12032, pp. 200-206)"
],
"network_data_format": {
"inputs": {
"image": {
"type": "image",
"format": "hounsfield",
"modality": "CT",
"num_channels": 1,
"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": 8,
"spatial_shape": [
96,
96,
96
],
"dtype": "float32",
"value_range": [
0,
1,
2,
3,
4,
5,
6,
7
],
"is_patch_data": true,
"channel_def": {
"0": "background",
"1": "artery",
"2": "portal vein",
"3": "liver",
"4": "spleen",
"5": "stomach",
"6": "gallbladder",
"7": "pancreas"
}
}
}
}
}
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