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{
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
"version": "0.4.1",
"changelog": {
"0.4.1": "update params to supprot torch.jit.trace torchscript conversion",
"0.4.0": "add name tag",
"0.3.9": "use ITKreader to avoid mass logs at image loading",
"0.3.8": "restructure readme to match updated template",
"0.3.7": "Update metric in metadata",
"0.3.6": "Update ckpt drive link",
"0.3.5": "Update figure and benchmarking",
"0.3.4": "Update figure link in readme",
"0.3.3": "Update, verify MONAI 1.0.1 and Pytorch 1.13.0",
"0.3.2": "enhance readme on commands example",
"0.3.1": "fix license Copyright error",
"0.3.0": "update license files",
"0.2.0": "unify naming",
"0.1.0": "complete the model package",
"0.0.1": "initialize the model package structure"
},
"monai_version": "1.0.1",
"pytorch_version": "1.13.0",
"numpy_version": "1.21.2",
"optional_packages_version": {
"nibabel": "3.2.1",
"pytorch-ignite": "0.4.8",
"einops": "0.4.1"
},
"name": "Swin UNETR BTCV segmentation",
"task": "BTCV multi-organ segmentation",
"description": "A pre-trained model for volumetric (3D) multi-organ segmentation from CT image",
"authors": "MONAI team",
"copyright": "Copyright (c) MONAI Consortium",
"data_source": "RawData.zip from https://www.synapse.org/#!Synapse:syn3193805/wiki/217752/",
"data_type": "nibabel",
"image_classes": "single channel data, intensity scaled to [0, 1]",
"label_classes": "multi-channel data,0:background,1:spleen, 2:Right Kidney, 3:Left Kideny, 4:Gallbladder, 5:Esophagus, 6:Liver, 7:Stomach, 8:Aorta, 9:IVC, 10:Portal and Splenic Veins, 11:Pancreas, 12:Right adrenal gland, 13:Left adrenal gland",
"pred_classes": "14 channels OneHot data, 0:background,1:spleen, 2:Right Kidney, 3:Left Kideny, 4:Gallbladder, 5:Esophagus, 6:Liver, 7:Stomach, 8:Aorta, 9:IVC, 10:Portal and Splenic Veins, 11:Pancreas, 12:Right adrenal gland, 13:Left adrenal gland",
"eval_metrics": {
"mean_dice": 0.8269
},
"intended_use": "This is an example, not to be used for diagnostic purposes",
"references": [
"Hatamizadeh, Ali, et al. 'Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images. arXiv preprint arXiv:2201.01266 (2022). https://arxiv.org/abs/2201.01266.",
"Tang, Yucheng, et al. 'Self-supervised pre-training of swin transformers for 3d medical image analysis. arXiv preprint arXiv:2111.14791 (2021). https://arxiv.org/abs/2111.14791."
],
"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": 14,
"spatial_shape": [
96,
96,
96
],
"dtype": "float32",
"value_range": [
0,
1
],
"is_patch_data": true,
"channel_def": {
"0": "background",
"1": "spleen",
"2": "Right Kidney",
"3": "Left Kideny",
"4": "Gallbladder",
"5": "Esophagus",
"6": "Liver",
"7": "Stomach",
"8": "Aorta",
"9": "IVC",
"10": "Portal and Splenic Veins",
"11": "Pancreas",
"12": "Right adrenal gland",
"13": "Left adrenal gland"
}
}
}
}
}
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