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total/app.json
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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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"terminology": {
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"1": { "name": "spleen", "color": "#4B79EA" },
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"2": { "name": "kidney_right", "color": "#A8EA4B" },
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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": { "name": "spleen", "color": "#4B79EA" },
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"2": { "name": "kidney_right", "color": "#A8EA4B" },
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