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

TY - CHAP

T1 - A Persistent Homology-Based Topological Loss Function for Multi-class CNN Segmentation of Cardiac MRI

AU - Byrne, Nick

AU - Clough, James R.

AU - Montana, Giovanni

AU - King, Andrew P.

N1 - Funding Information: This research has been supported by Korean Ministry Information and Communication (Grant No. 02MC2910). Publisher Copyright: © 2021, Springer Nature Switzerland AG. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - CNN

KW - Image segmentation

KW - MRI

KW - Topology

UR - http://www.scopus.com/inward/record.url?scp=85101494372&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-68107-4_1

DO - 10.1007/978-3-030-68107-4_1

M3 - Conference paper

AN - SCOPUS:85101494372

SN - 9783030681067

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 3

EP - 13

BT - Statistical 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

A2 - Puyol Anton, Esther

A2 - Pop, Mihaela

A2 - Sermesant, Maxime

A2 - Campello, Victor

A2 - Lalande, Alain

A2 - Lekadir, Karim

A2 - Suinesiaputra, Avan

A2 - Camara, Oscar

A2 - Young, Alistair

PB - Springer Science and Business Media Deutschland GmbH

T2 - 11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020

Y2 - 4 October 2020 through 4 October 2020

ER -