bibtext: @InProceedings{ Li_Universal_MICCAI2024,
                   author = { Li, Liu and Wang, Hanchun and Baugh, Matthew and Ma, Qiang and Zhang, Weitong and Ouyang, Cheng and Rueckert, Daniel and Kainz, Bernhard },
                   title = { { Universal Topology Refinement for Medical Image Segmentation with Polynomial Feature Synthesis } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15009 },
                   month = {October},
                   pages = { pending },
                }
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.
title: Universal Topology Refinement for Medical Image Segmentation with Polynomial Feature Synthesis
authors: Li, Liu Wang, Hanchun Baugh, Matthew Ma, Qiang Zhang, Weitong Ouyang, Cheng Rueckert, Daniel Kainz, Bernhard