Explicit Topological Priors for Deep-Learning Based Image Segmentation Using Persistent Homology
Clough JR, Oksuz I, Byrne N, Schnabel JA, King AP (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer Verlag
Book Volume: 11492 LNCS
Pages Range: 16-28
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Hong Kong, CHN
ISBN: 9783030203504
DOI: 10.1007/978-3-030-20351-1_2
Abstract
We present a novel method to explicitly incorporate topological prior knowledge into deep learning based segmentation, which is, to our knowledge, the first work to do so. Our method uses the concept of persistent homology, a tool from topological data analysis, to capture high-level topological characteristics of segmentation results in a way which is differentiable with respect to the pixelwise probability of being assigned to a given class. The topological prior knowledge consists of the sequence of desired Betti numbers of the segmentation. As a proof-of-concept we demonstrate our approach by applying it to the problem of left-ventricle segmentation of cardiac MR images of subjects from the UK Biobank dataset, where we show that it improves segmentation performance in terms of topological correctness without sacrificing pixelwise accuracy.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Clough, J.R., Oksuz, I., Byrne, N., Schnabel, J.A., & King, A.P. (2019). Explicit Topological Priors for Deep-Learning Based Image Segmentation Using Persistent Homology. In Siqi Bao, Albert C.S. Chung, James C. Gee, Paul A. Yushkevich (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 16-28). Hong Kong, CHN: Springer Verlag.
MLA:
Clough, James R., et al. "Explicit Topological Priors for Deep-Learning Based Image Segmentation Using Persistent Homology." Proceedings of the 26th International Conference on Information Processing in Medical Imaging, IPMI 2019, Hong Kong, CHN Ed. Siqi Bao, Albert C.S. Chung, James C. Gee, Paul A. Yushkevich, Springer Verlag, 2019. 16-28.
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