Topologically Faithful Multi-class Segmentation in Medical Images

• URL: http://arxiv.org/abs/2403.11001v2
• Date: Wed, 09 Oct 2024 17:44:14 GMT
• Title: Topologically Faithful Multi-class Segmentation in Medical Images
• Authors: Alexander H. Berger, Nico Stucki, Laurin Lux, Vincent Buergin, Suprosanna Shit, Anna Banaszak, Daniel Rueckert, Ulrich Bauer, Johannes C. Paetzold,
• Abstract summary: We propose a general loss function for topologically faithful multi-class segmentation. We project the N-class segmentation problem to N single-class segmentation tasks. Our loss formulation significantly enhances topological correctness in cardiac, cell, artery-vein, and Circle of Willis segmentation.
• Score: 43.6770098513581
• License:
• 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 on induced matchings of persistence barcodes. We project the N-class segmentation problem to N single-class segmentation tasks, which allows us to use 1-parameter persistent homology, making training of neural networks computationally feasible. We validate our method on a comprehensive set of four medical datasets with highly variant topological characteristics. Our loss formulation significantly enhances topological correctness in cardiac, cell, artery-vein, and Circle of Willis segmentation.

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