A Persistent Homology-Based Topological Loss Function for Multi-class CNN Segmentation of Cardiac MRI

Nick Byrne*, James R. Clough, Giovanni Montana, Andrew P. King

*Corresponding author for this work

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

10 Citations (Scopus)

Abstract

With respect to spatial overlap, CNN-based segmentation of short axis cardiovascular magnetic resonance (CMR) images has achieved a level of performance consistent with inter observer variation. However, conventional training procedures frequently depend on pixel-wise loss functions, limiting optimisation with respect to extended or global features. As a result, inferred segmentations can lack spatial coherence, including spurious connected components or holes. Such results are implausible, violating the anticipated topology of image segments, which is frequently known a priori. Addressing this challenge, published work has employed persistent homology, constructing topological loss functions for the evaluation of image segments against an explicit prior. Building a richer description of segmentation topology by considering all possible labels and label pairs, we extend these losses to the task of multi-class segmentation. These topological priors allow us to resolve all topological errors in a subset of 150 examples from the ACDC short axis CMR training data set, without sacrificing overlap performance.

Original languageEnglish
Title of host publicationStatistical Atlases and Computational Models of the Heart. MandMs and EMIDEC Challenges - 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers
EditorsEsther Puyol Anton, Mihaela Pop, Maxime Sermesant, Victor Campello, Alain Lalande, Karim Lekadir, Avan Suinesiaputra, Oscar Camara, Alistair Young
PublisherSpringer Science and Business Media Deutschland GmbH
Pages3-13
Number of pages11
ISBN (Print)9783030681067
DOIs
Publication statusPublished - 2021
Event11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020 - Lima, Peru
Duration: 4 Oct 20204 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12592 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020
Country/TerritoryPeru
CityLima
Period4/10/20204/10/2020

Keywords

  • CNN
  • Image segmentation
  • MRI
  • Topology

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