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Update total/app.json

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  1. total/app.json +120 -1
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@@ -3,5 +3,124 @@
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  "short_description": "<b>Description:</b><br>KonfAI-accelerated adaptation of <a href='https://github.com/wasserth/TotalSegmentator'>TotalSegmentator</a>, delivering fast whole-body CT segmentation of <b>118 anatomical structures</b> (1.5 mm resolution) with reduced inference cost compared to the original nnU-Net implementation.<br><br><b>How to cite:</b><br><cite>J. Wasserthal et al., <i>TotalSegmentator: Robust Segmentation of 104 Anatomical Structures in CT Images</i>, Radiology: AI, 2023.</cite>",
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  "description": "<b>Description:</b><br>This model is an optimized adaptation of the original <a href='https://github.com/wasserth/TotalSegmentator'>TotalSegmentator</a> for the <b>KonfAI</b> deep learning framework.<br><br><b>Capabilities:</b><br>• Segmentation of <b>118 anatomical classes</b> covering organs, bones, muscles, and vessels<br>• Enhanced runtime and memory efficiency vs. the original nnU-Net implementation<br>• High-resolution input: <b>1.5 mm isotropic</b><br><br><b>Training data:</b><br>Trained on a diverse dataset of <b>1204 whole-body CT examinations</b> including different scanners, acquisition settings, contrast phases, and major pathologies (27 organs, 59 bones, 10 muscles, 8 vessels), with manual expert-reviewed annotations<br><br><b>How to cite:</b><br><cite>J. Wasserthal et al., <i>TotalSegmentator: Robust Segmentation of 104 Anatomical Structures in CT Images</i>, Radiology: AI, 2023.</cite>",
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  "tta": 0,
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- "mc_dropout": 0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  }
 
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  "short_description": "<b>Description:</b><br>KonfAI-accelerated adaptation of <a href='https://github.com/wasserth/TotalSegmentator'>TotalSegmentator</a>, delivering fast whole-body CT segmentation of <b>118 anatomical structures</b> (1.5 mm resolution) with reduced inference cost compared to the original nnU-Net implementation.<br><br><b>How to cite:</b><br><cite>J. Wasserthal et al., <i>TotalSegmentator: Robust Segmentation of 104 Anatomical Structures in CT Images</i>, Radiology: AI, 2023.</cite>",
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  "description": "<b>Description:</b><br>This model is an optimized adaptation of the original <a href='https://github.com/wasserth/TotalSegmentator'>TotalSegmentator</a> for the <b>KonfAI</b> deep learning framework.<br><br><b>Capabilities:</b><br>• Segmentation of <b>118 anatomical classes</b> covering organs, bones, muscles, and vessels<br>• Enhanced runtime and memory efficiency vs. the original nnU-Net implementation<br>• High-resolution input: <b>1.5 mm isotropic</b><br><br><b>Training data:</b><br>Trained on a diverse dataset of <b>1204 whole-body CT examinations</b> including different scanners, acquisition settings, contrast phases, and major pathologies (27 organs, 59 bones, 10 muscles, 8 vessels), with manual expert-reviewed annotations<br><br><b>How to cite:</b><br><cite>J. Wasserthal et al., <i>TotalSegmentator: Robust Segmentation of 104 Anatomical Structures in CT Images</i>, Radiology: AI, 2023.</cite>",
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  "tta": 0,
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+ "mc_dropout": 0,
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+ "terminology": {
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+ "1": "spleen",
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+ "2": "kidney_right",
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+ "3": "kidney_left",
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+ "4": "gallbladder",
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+ "5": "liver",
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+ "6": "stomach",
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+ "7": "pancreas",
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+ "8": "adrenal_gland_right",
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+ "9": "adrenal_gland_left",
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+ "10": "lung_upper_lobe_left",
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+ "11": "lung_lower_lobe_left",
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+ "12": "lung_upper_lobe_right",
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+ "13": "lung_middle_lobe_right",
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+ "14": "lung_lower_lobe_right",
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+ "15": "esophagus",
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+ "16": "trachea",
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+ "17": "thyroid_gland",
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+ "18": "small_bowel",
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+ "19": "duodenum",
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+ "20": "colon",
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+ "21": "urinary_bladder",
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+ "22": "prostate",
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+ "23": "kidney_cyst_left",
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+ "24": "kidney_cyst_right",
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+ "25": "sacrum",
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+ "26": "vertebrae_S1",
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+ "27": "vertebrae_L5",
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+ "28": "vertebrae_L4",
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+ "29": "vertebrae_L3",
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+ "30": "vertebrae_L2",
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+ "31": "vertebrae_L1",
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+ "32": "vertebrae_T12",
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+ "33": "vertebrae_T11",
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+ "34": "vertebrae_T10",
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+ "35": "vertebrae_T9",
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+ "36": "vertebrae_T8",
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+ "37": "vertebrae_T7",
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+ "38": "vertebrae_T6",
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+ "39": "vertebrae_T5",
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+ "40": "vertebrae_T4",
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+ "41": "vertebrae_T3",
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+ "42": "vertebrae_T2",
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+ "43": "vertebrae_T1",
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+ "44": "vertebrae_C7",
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+ "45": "vertebrae_C6",
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+ "46": "vertebrae_C5",
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+ "47": "vertebrae_C4",
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+ "48": "vertebrae_C3",
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+ "49": "vertebrae_C2",
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+ "50": "vertebrae_C1",
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+ "51": "heart",
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+ "52": "aorta",
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+ "53": "pulmonary_vein",
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+ "54": "brachiocephalic_trunk",
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+ "55": "subclavian_artery_right",
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+ "56": "subclavian_artery_left",
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+ "57": "common_carotid_artery_right",
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+ "58": "common_carotid_artery_left",
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+ "59": "brachiocephalic_vein_left",
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+ "60": "brachiocephalic_vein_right",
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+ "61": "atrial_appendage_left",
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+ "62": "superior_vena_cava",
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+ "63": "inferior_vena_cava",
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+ "64": "portal_vein_and_splenic_vein",
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+ "65": "iliac_artery_left",
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+ "66": "iliac_artery_right",
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+ "67": "iliac_vena_left",
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+ "68": "iliac_vena_right",
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+ "69": "humerus_left",
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+ "70": "humerus_right",
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+ "71": "scapula_left",
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+ "72": "scapula_right",
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+ "73": "clavicula_left",
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+ "74": "clavicula_right",
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+ "75": "femur_left",
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+ "76": "femur_right",
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+ "77": "hip_left",
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+ "78": "hip_right",
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+ "79": "spinal_cord",
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+ "80": "gluteus_maximus_left",
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+ "81": "gluteus_maximus_right",
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+ "82": "gluteus_medius_left",
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+ "83": "gluteus_medius_right",
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+ "84": "gluteus_minimus_left",
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+ "85": "gluteus_minimus_right",
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+ "86": "autochthon_left",
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+ "87": "autochthon_right",
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+ "88": "iliopsoas_left",
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+ "89": "iliopsoas_right",
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+ "90": "brain",
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+ "91": "skull",
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+ "92": "rib_left_1",
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+ "93": "rib_left_2",
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+ "94": "rib_left_3",
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+ "95": "rib_left_4",
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+ "96": "rib_left_5",
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+ "97": "rib_left_6",
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+ "98": "rib_left_7",
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+ "99": "rib_left_8",
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+ "100": "rib_left_9",
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+ "101": "rib_left_10",
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+ "102": "rib_left_11",
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+ "103": "rib_left_12",
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+ "104": "rib_right_1",
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+ "105": "rib_right_2",
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+ "106": "rib_right_3",
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+ "107": "rib_right_4",
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+ "108": "rib_right_5",
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+ "109": "rib_right_6",
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+ "110": "rib_right_7",
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+ "111": "rib_right_8",
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+ "112": "rib_right_9",
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+ "113": "rib_right_10",
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+ "114": "rib_right_11",
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+ "115": "rib_right_12",
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+ "116": "sternum",
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+ "117": "costal_cartilages"
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+ }
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  }