Universal Topology Refinement for Medical Image Segmentation with Polynomial Feature Synthesis
<|reference_start|>Universal Topology Refinement for Medical Image Segmentation with Polynomial Feature Synthesis: 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.<|reference_end|>
@article{li2024universal,
    title={Universal Topology Refinement for Medical Image Segmentation with
  Polynomial Feature Synthesis},
    author={Liu Li, Hanchun Wang, Matthew Baugh, Qiang Ma, Weitong Zhang, Cheng
  Ouyang, Daniel Rueckert, Bernhard Kainz},
    journal={arXiv preprint arXiv:2409.09796},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09796},
    primaryClass={eess.IV cs.CV}
}