Universal Topology Refinement for Medical Image Segmentation with Polynomial Feature Synthesis
Li L, Wang H, Baugh M, Ma Q, Zhang W, Ouyang C, Rueckert D, Kainz B (2024)
Publication Type: Conference contribution
Publication year: 2024
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 15009 LNCS
Pages Range: 670-680
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Marrakesh, MAR
ISBN: 9783031721137
DOI: 10.1007/978-3-031-72114-4_64
Abstract
Although existing medical image segmentation methods provide impressive pixel-wise accuracy, they often neglect topological correctness, making their segmentations unusable for many downstream tasks. One option is to retrain such models whilst including a topologydriven loss component. However, this is computationally expensive and often impractical. A better solution would be to have a versatile plug-and-play topology refinement method that is compatible with any domain-specific segmentation pipeline. Directly training a postprocessing model to mitigate topological errors often fails as such models tend to be biased towards the topological errors of a target segmentation network. The diversity of these errors is confined to the information provided by a labelled training set, which is especially problematic for small datasets. Our method solves this problem by training a model-agnostic topology refinement network with synthetic segmentations that cover a wide variety of topological errors. Inspired by the Stone-Weierstrass theorem, we synthesize topology-perturbation masks with randomly sampled coefficients of orthogonal polynomial bases, which ensures a complete and unbiased representation. Practically, we verified the efficiency and effectiveness of our methods as being compatible with multiple families of polynomial bases, and show evidence that our universal plug-and-play topology refinement network outperforms both existing topology-driven learning-based and post-processing methods. We also show that combining our method with learning-based models provides an effortless add-on, which can further improve the performance of existing approaches.
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APA:
Li, L., Wang, H., Baugh, M., Ma, Q., Zhang, W., Ouyang, C.,... Kainz, B. (2024). Universal Topology Refinement for Medical Image Segmentation with Polynomial Feature Synthesis. In Marius George Linguraru, Qi Dou, Aasa Feragen, Stamatia Giannarou, Ben Glocker, Karim Lekadir, Julia A. Schnabel (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 670-680). Marrakesh, MAR: Springer Science and Business Media Deutschland GmbH.
MLA:
Li, Liu, et al. "Universal Topology Refinement for Medical Image Segmentation with Polynomial Feature Synthesis." Proceedings of the 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024, Marrakesh, MAR Ed. Marius George Linguraru, Qi Dou, Aasa Feragen, Stamatia Giannarou, Ben Glocker, Karim Lekadir, Julia A. Schnabel, Springer Science and Business Media Deutschland GmbH, 2024. 670-680.
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