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total-3mm/metadata.json
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"display_name": "
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"short_description": "<b>Description:</b><br>Lightweight KonfAI adaptation of <a href='https://github.com/wasserth/TotalSegmentator'>TotalSegmentator</a> trained at <b>3 mm resolution</b>, reducing GPU/RAM requirements while segmenting <b>118 anatomical structures</b> in whole-body CT.<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>KonfAI-optimized version of the original nnU-Net-based TotalSegmentator 3 mm model.<br><br><b>Capabilities:</b><br>• Whole-body CT segmentation of <b>118 structures</b> (organs, bones, muscles, vessels)<br>• Reduced computational footprint for lower memory and faster throughput<br>• <b>3 mm isotropic</b> inference for easier deployment on large datasets<br><br><b>Training data:</b><br>Trained on <b>1204 clinically-derived CT scans</b> with strong diversity in contrast phases, scanner types and pathologies, with expert-reviewed manual annotations<br><br><br><b>How to cite:</b><br><cite>J. Wasserthal et al., 2023.</cite>",
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"tta": 0,
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"display_name": "TotalSegmentator 3mm",
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"short_description": "<b>Description:</b><br>Lightweight KonfAI adaptation of <a href='https://github.com/wasserth/TotalSegmentator'>TotalSegmentator</a> trained at <b>3 mm resolution</b>, reducing GPU/RAM requirements while segmenting <b>118 anatomical structures</b> in whole-body CT.<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>KonfAI-optimized version of the original nnU-Net-based TotalSegmentator 3 mm model.<br><br><b>Capabilities:</b><br>• Whole-body CT segmentation of <b>118 structures</b> (organs, bones, muscles, vessels)<br>• Reduced computational footprint for lower memory and faster throughput<br>• <b>3 mm isotropic</b> inference for easier deployment on large datasets<br><br><b>Training data:</b><br>Trained on <b>1204 clinically-derived CT scans</b> with strong diversity in contrast phases, scanner types and pathologies, with expert-reviewed manual annotations<br><br><br><b>How to cite:</b><br><cite>J. Wasserthal et al., 2023.</cite>",
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"tta": 0,
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