Topologically Faithful Multi-class Segmentation in Medical Images
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913539290038272 |
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| author | Berger, Alexander H. Stucki, Nico Lux, Laurin Buergin, Vincent Shit, Suprosanna Banaszak, Anna Rueckert, Daniel Bauer, Ulrich Paetzold, Johannes C. |
| author_facet | Berger, Alexander H. Stucki, Nico Lux, Laurin Buergin, Vincent Shit, Suprosanna Banaszak, Anna Rueckert, Daniel Bauer, Ulrich Paetzold, Johannes C. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_11001 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Topologically Faithful Multi-class Segmentation in Medical Images Berger, Alexander H. Stucki, Nico Lux, Laurin Buergin, Vincent Shit, Suprosanna Banaszak, Anna Rueckert, Daniel Bauer, Ulrich Paetzold, Johannes C. Image and Video Processing Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Topologically Faithful Multi-class Segmentation in Medical Images |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2403.11001 |