AG-TAL loss improves multiclass Circle of Willis segmentation to 80.85% average Dice with 1-3% gains on small arteries across multi-center datasets by embedding anatomical priors into topology-aware terms.
Benchmarking the CoW with the TopCoW Challenge: Topology-aware anatomical segmentation of the Circle of Willis for CTA and MRA
2 Pith papers cite this work. Polarity classification is still indexing.
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VesselSim trains a 3D vessel segmentation model exclusively on 16,500 synthetic angiographic volumes generated by stochastic branching simulation and achieves competitive zero-shot performance on real clinical datasets via test-time mask reconstruction adaptation.
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AG-TAL: Anatomically-Guided Topology-Aware Loss for Multiclass Segmentation of the Circle of Willis Using Large-Scale Multi-Center Datasets
AG-TAL loss improves multiclass Circle of Willis segmentation to 80.85% average Dice with 1-3% gains on small arteries across multi-center datasets by embedding anatomical priors into topology-aware terms.
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VesselSim: learning 3D blood vessel segmentation without expert annotations
VesselSim trains a 3D vessel segmentation model exclusively on 16,500 synthetic angiographic volumes generated by stochastic branching simulation and achieves competitive zero-shot performance on real clinical datasets via test-time mask reconstruction adaptation.