One Model, Two Label Spaces, and a Boundary Tradeoff
TL;DR for operators A healthcare AI team may want one segmentation system for uterine ultrasound and MRI, even when the datasets identify different anatomical structures. Topology-informed Prompt-conditioned Universal Segmentation (TPUS) addresses this problem by combining dataset-specific input processing with shared feature learning and class-conditioned prediction.1 Across 82 held-out cases from two datasets, TPUS leads seven competing complete methods on segmentation overlap and average boundary distance. Yet its improvements over the strongest baseline are modest. ...