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{
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
"version": "0.1.9",
"changelog": {
"0.1.9": "fix the wrong GPU index issue of multi-node",
"0.1.8": "Update evalaute doc, GPU usage details, and dataset preparation instructions",
"0.1.7": "remove error dollar symbol in readme",
"0.1.6": "add RAM usage with CacheDataset and GPU consumtion warning",
"0.1.5": "fix mgpu finalize issue",
"0.1.4": "Update README Formatting",
"0.1.3": "add non-deterministic note",
"0.1.2": "Update figure with links",
"0.1.1": "adapt to BundleWorkflow interface and val metric",
"0.1.0": "complete the model package",
"0.0.1": "initialize the model package structure"
},
"monai_version": "1.2.0",
"pytorch_version": "1.13.1",
"numpy_version": "1.22.2",
"optional_packages_version": {
"nibabel": "4.0.1",
"pytorch-ignite": "0.4.9"
},
"name": "Whole body CT segmentation",
"task": "TotalSegmentator Segmentation",
"description": "A pre-trained SegResNet model for volumetric (3D) segmentation of the 104 whole body segments",
"authors": "MONAI team",
"copyright": "Copyright (c) MONAI Consortium",
"data_source": "TotalSegmentator",
"data_type": "nibabel",
"image_classes": "104 foreground channels, 0 channel for the background, intensity scaled to [0, 1]",
"label_classes": "0 is the background, others are whole body segments",
"pred_classes": "0 is the background, 104 other chanels are whole body segments",
"eval_metrics": {
"mean_dice": 0.8
},
"intended_use": "This is an example, not to be used for diagnostic purposes",
"references": [
"Wasserthal, J., Meyer, M., Breit, H.C., Cyriac, J., Yang, S. and Segeroth, M., 2022. TotalSegmentator: robust segmentation of 104 anatomical structures in CT images. arXiv preprint arXiv:2208.05868.",
"Myronenko, A., Siddiquee, M.M.R., Yang, D., He, Y. and Xu, D., 2022. Automated head and neck tumor segmentation from 3D PET/CT. arXiv preprint arXiv:2209.10809.",
"Tang, Y., Gao, R., Lee, H.H., Han, S., Chen, Y., Gao, D., Nath, V., Bermudez, C., Savona, M.R., Abramson, R.G. and Bao, S., 2021. High-resolution 3D abdominal segmentation with random patch network fusion. Medical image analysis, 69, p.101894."
],
"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": 105,
"spatial_shape": [
96,
96,
96
],
"dtype": "float32",
"value_range": [
0,
104
],
"is_patch_data": true,
"channel_def": {
"0": "background",
"1": "spleen",
"2": "kidney_right",
"3": "kidney_left",
"4": "gallbladder",
"5": "liver",
"6": "stomach",
"7": "aorta",
"8": "inferior_vena_cava",
"9": "portal_vein_and_splenic_vein",
"10": "pancreas",
"11": "adrenal_gland_right",
"12": "adrenal_gland_left",
"13": "lung_upper_lobe_left",
"14": "lung_lower_lobe_left",
"15": "lung_upper_lobe_right",
"16": "lung_middle_lobe_right",
"17": "lung_lower_lobe_right",
"18": "vertebrae_L5",
"19": "vertebrae_L4",
"20": "vertebrae_L3",
"21": "vertebrae_L2",
"22": "vertebrae_L1",
"23": "vertebrae_T12",
"24": "vertebrae_T11",
"25": "vertebrae_T10",
"26": "vertebrae_T9",
"27": "vertebrae_T8",
"28": "vertebrae_T7",
"29": "vertebrae_T6",
"30": "vertebrae_T5",
"31": "vertebrae_T4",
"32": "vertebrae_T3",
"33": "vertebrae_T2",
"34": "vertebrae_T1",
"35": "vertebrae_C7",
"36": "vertebrae_C6",
"37": "vertebrae_C5",
"38": "vertebrae_C4",
"39": "vertebrae_C3",
"40": "vertebrae_C2",
"41": "vertebrae_C1",
"42": "esophagus",
"43": "trachea",
"44": "heart_myocardium",
"45": "heart_atrium_left",
"46": "heart_ventricle_left",
"47": "heart_atrium_right",
"48": "heart_ventricle_right",
"49": "pulmonary_artery",
"50": "brain",
"51": "iliac_artery_left",
"52": "iliac_artery_right",
"53": "iliac_vena_left",
"54": "iliac_vena_right",
"55": "small_bowel",
"56": "duodenum",
"57": "colon",
"58": "rib_left_1",
"59": "rib_left_2",
"60": "rib_left_3",
"61": "rib_left_4",
"62": "rib_left_5",
"63": "rib_left_6",
"64": "rib_left_7",
"65": "rib_left_8",
"66": "rib_left_9",
"67": "rib_left_10",
"68": "rib_left_11",
"69": "rib_left_12",
"70": "rib_right_1",
"71": "rib_right_2",
"72": "rib_right_3",
"73": "rib_right_4",
"74": "rib_right_5",
"75": "rib_right_6",
"76": "rib_right_7",
"77": "rib_right_8",
"78": "rib_right_9",
"79": "rib_right_10",
"80": "rib_right_11",
"81": "rib_right_12",
"82": "humerus_left",
"83": "humerus_right",
"84": "scapula_left",
"85": "scapula_right",
"86": "clavicula_left",
"87": "clavicula_right",
"88": "femur_left",
"89": "femur_right",
"90": "hip_left",
"91": "hip_right",
"92": "sacrum",
"93": "face",
"94": "gluteus_maximus_left",
"95": "gluteus_maximus_right",
"96": "gluteus_medius_left",
"97": "gluteus_medius_right",
"98": "gluteus_minimus_left",
"99": "gluteus_minimus_right",
"100": "autochthon_left",
"101": "autochthon_right",
"102": "iliopsoas_left",
"103": "iliopsoas_right",
"104": "urinary_bladder"
}
}
}
}
}
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