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{
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
"version": "0.2.7",
"changelog": {
"0.2.7": "enhance metadata with improved descriptions",
"0.2.6": "update to huggingface hosting",
"0.2.5": "use monai 1.4 and update large files",
"0.2.4": "update to use monai 1.3.1",
"0.2.3": "add load_pretrain flag for infer",
"0.2.2": "add checkpoint loader for infer",
"0.2.1": "remove meta_dict usage",
"0.2.0": "add support for TensorRT conversion and inference",
"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": "1.4.0",
"pytorch_version": "2.4.0",
"numpy_version": "1.24.4",
"required_packages_version": {
"itk": "5.4.0",
"nibabel": "5.2.1",
"pytorch-ignite": "0.4.11",
"tensorboard": "2.17.0"
},
"supported_apps": {},
"name": "Whole Body CT Segmentation",
"task": "Multi-organ Segmentation in Whole Body CT Scans",
"description": "A SegResNet-based volumetric segmentation model that segments 104 distinct anatomical structures from CT scans. The model processes 96x96x96 pixel patches and provides segmentation masks for major organs, bones, muscles, and vascular structures throughout the body, trained on TotalSegmentator data.",
"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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