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Robust Segmentation via Topology Violation Detection and Feature Synthesis

Li L, Ma Q, Ouyang C, Li Z, Meng Q, Zhang W, Qiao M, Kyriakopoulou V, Hajnal JV, Rueckert D, Kainz B (2023)

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Publication Type: Conference contribution, Original article

Publication year: 2023

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 14223 LNCS

Pages Range: 67-77

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Vancouver, BC

ISBN: 9783031439001

DOI: 10.1007/978-3-031-43901-8_7

Abstract

Despite recent progress of deep learning-based medical image segmentation techniques, fully automatic results often fail to meet clinically acceptable accuracy, especially when topological constraints should be observed, e.g., closed surfaces. Although modern image segmentation methods show promising results when evaluated based on conventional metrics such as the Dice score or Intersection-over-Union, these metrics do not reflect the correctness of a segmentation in terms of a required topological genus. Existing approaches estimate and constrain the topological structure via persistent homology (PH). However, these methods are not computationally efficient as calculating PH is not differentiable. To overcome this problem, we propose a novel approach for topological constraints based on the multi-scale Euler Characteristic (EC). To mitigate computational complexity, we propose a fast formulation for the EC that can inform the learning process of arbitrary segmentation networks via topological violation maps. Topological performance is further facilitated through a corrective convolutional network block. Our experiments on two datasets show that our method can significantly improve topological correctness.

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How to cite

APA:

Li, L., Ma, Q., Ouyang, C., Li, Z., Meng, Q., Zhang, W.,... Kainz, B. (2023). Robust Segmentation via Topology Violation Detection and Feature Synthesis. In Hayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 67-77). Vancouver, BC, CA: Springer Science and Business Media Deutschland GmbH.

MLA:

Li, Liu, et al. "Robust Segmentation via Topology Violation Detection and Feature Synthesis." Proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, Vancouver, BC Ed. Hayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor, Springer Science and Business Media Deutschland GmbH, 2023. 67-77.

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