Topologically Faithful Multi-class Segmentation in Medical Images
<|reference_start|>Topologically Faithful Multi-class Segmentation in Medical Images: 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.<|reference_end|>
@article{berger2024topologically,
    title={Topologically Faithful Multi-class Segmentation in Medical Images},
    author={Alexander H. Berger, Nico Stucki, Laurin Lux, Vincent Buergin,
  Suprosanna Shit, Anna Banaszak, Daniel Rueckert, Ulrich Bauer, Johannes C.
  Paetzold},
    journal={MICCAI 2024, Lecture Notes in Computer Science, vol. 15008, pp.
  721-731, 2024},
    year={2024},
    doi={10.1007/978-3-031-72111-3_68},
    archivePrefix={arXiv},
    eprint={2403.11001},
    primaryClass={eess.IV cs.CV cs.LG}
}