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"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
"version": "0.3.6",
"changelog": {
"0.3.6": "enhance metadata with improved descriptions",
"0.3.5": "update to huggingface hosting",
"0.3.4": "support monai 1.4",
"0.3.3": "add invertd transformation",
"0.3.2": "add name tag",
"0.3.1": "fix license Copyright error",
"0.3.0": "update license files",
"0.2.0": "unify naming",
"0.1.1": "add torchscript model",
"0.1.0": "complete the model package"
},
"monai_version": "1.4.0",
"pytorch_version": "2.4.0",
"numpy_version": "1.24.4",
"optional_packages_version": {
"nibabel": "5.2.1",
"itk": "5.4.0",
"pytorch-ignite": "0.4.11",
"pandas": "2.2.1"
},
"name": "Prostate MRI Anatomy",
"task": "Segmentation of Peripheral Zone and Central Gland in Prostate MRI",
"description": "A 3D segmentation model that differentiates between central gland and peripheral zone within the prostate in MRI images. The model processes 96x96x96 pixel patches and provides segmentation masks.",
"authors": "Keno Bressem",
"copyright": "Copyright (c) Keno Bressem",
"data_source": "Prostate158 from 10.5281/zenodo.6481141",
"data_type": "nifti",
"image_classes": "single channel data, intensity scaled to [0, 1]",
"label_classes": "singe channel data, 1 central gland, 2 periheral zone, 0 is everything else",
"pred_classes": "3 channels OneHot data, channel 1 central gland, channel 2 is peripheral zone, channel 0 is background",
"eval_metrics": {
"mean_dice": {
"central gland": 0.88,
"peripheral zone": 0.75
}
},
"intended_use": "This is an example, not to be used for diagnostic purposes",
"references": [
"Adams, L. C., Makowski, M. R., Engel, G., Rattunde, M., Busch, F., Asbach, P., ... & Bressem, K. K. (2022). Prostate158-An expert-annotated 3T MRI dataset and algorithm for prostate cancer detection. Computers in Biology and Medicine, 148, 105817."
],
"network_data_format": {
"inputs": {
"image": {
"type": "image",
"format": "magnitude",
"modality": "MR",
"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": "labels",
"num_channels": 3,
"spatial_shape": [
96,
96,
96
],
"dtype": "float32",
"value_range": [],
"is_patch_data": true,
"channel_def": {
"0": "background",
"1": "central gland",
"2": "peripheral zone"
}
}
}
}
}
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