Universal topology refinement for medical image segmentation with polynomial feature synthesis
File(s)
Accepted version
OA Location
Author(s)
Type
Conference Paper
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 topology-driven 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 post-processing 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.
Editor(s)
Linguraru, MG
Dou, Q
Feragen, A
Giannarou, S
Glocker, B
Lekadir, K
Schnabel, JA
Date Issued
2024-10-03
Date Acceptance
2024-10-01
Citation
Medical Image Computing and Computer Assisted Intervention – MICCAI 2024, 2024, 15009, pp.670-680
ISBN
978-3-031-72113-7
ISSN
0302-9743
Publisher
Springer International Publishing AG
Start Page
670
End Page
680
Journal / Book Title
Medical Image Computing and Computer Assisted Intervention – MICCAI 2024
Volume
15009
Copyright Statement
Copyright © 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Source
27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
Subjects
Computer Science
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Life Sciences & Biomedicine
Radiology, Nuclear Medicine & Medical Imaging
Science & Technology
Segmentation
Synthesis
Technology
Topology
Publication Status
Published
Start Date
2024-10-06
Finish Date
2024-10-10
Coverage Spatial
Marrakesh, Morroco
Date Publish Online
2024-10-03