Explicit Topological Priors for Deep-Learning Based Image Segmentation Using Persistent Homology

James R. Clough*, Ilkay Oksuz, Nicholas Byrne, Julia A. Schnabel, Andrew P. King

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

35 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationInformation Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings
EditorsSiqi Bao, Albert C.S. Chung, James C. Gee, Paul A. Yushkevich
PublisherSpringer Verlag
Pages16-28
Number of pages13
ISBN (Print)9783030203504
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event26th International Conference on Information Processing in Medical Imaging, IPMI 2019 - Hong Kong, China
Duration: 2 Jun 20197 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11492 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Conference on Information Processing in Medical Imaging, IPMI 2019
Country/TerritoryChina
CityHong Kong
Period2/06/197/06/19

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Funding

A.P. King—This work was supported by an EPSRC programme Grant (EP/P001009/1) and the Wellcome EPSRC Centre for Medical Engineering at School of Biomedical Engineering and Imaging Sciences, King’s College London (WT 203148/Z/16/Z). This research has been conducted using the UK Biobank Resource under Application Number 40119. We would like to thank Nvidia for kindly donating the Quadro P6000 GPU used in this research.

FundersFunder number
Wellcome EPSRC Centre for Medical Engineering at School of Biomedical Engineering and Imaging Sciences
King’s College LondonWT 203148/Z/16/Z
Engineering and Physical Sciences Research CouncilEP/P001009/1

    Keywords

    • Cardiac MRI
    • Persistent homology
    • Segmentation
    • Topological data analysis
    • Topology

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