Topologically faithful multi-class segmentation in medical images

arXiv:2403.11001v1 Announce Type: cross
Abstract: Topological accuracy in medical image segmentation is a highly important property for downstream applications such as network analysis and flow modeling in vessels or cell counting. Recently, significant methodological advancements have brought well-founded concepts from algebraic topology to binary segmentation. However, these approaches have been underexplored in multi-class segmentation scenarios, where topological errors are common. We propose a general loss function for topologically faithful multi-class segmentation extending the recent Betti matching concept, which is based …

abstract accuracy analysis applications arxiv binary class concepts cs.cv cs.lg eess.iv flow however image images medical modeling network property segmentation topology type