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Self-supervised multi-organ segmentation for aging pattern exploration
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DOI：10.1016/j.bspc.2026.110153.png)
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Abstract
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• End-to-end SECT pipeline for abdominal organ morphology and proxy composition aging analysis. • AIG-MSPU leverages anatomical priors and multi-scale fusion for robust multi-organ segmentation. • BVTAMOS+ dataset: 6487 CT volumes; 170 labeled across 14 organs; 10-case pilot workflow evaluation. • Segmented regression with knots at 60 and 70 quantifies kidney and liver aging; proxy indices complement morphology.
Keywords:
multi-organ segmentation
aging analysis
self-supervised learning
abdominal organs
morphological aging
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Contour-aware network with class-wise convolutions for 3D abdominal multi-organ segmentation.png)
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