Modelling Cardiac Motion via Spatio-Temporal Graph Convolutional Networks to Boost the Diagnosis of Heart Conditions

Ping Lu, Wenjia Bai, Daniel Rueckert, J. Alison Noble

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

2 Scopus citations

Abstract

We present a novel spatio-temporal graph convolutional networks (ST-GCN) approach to learn spatio-temporal patterns of left ventricular (LV) motion in cardiac MR cine images for improving the characterization of heart conditions. Specifically, a novel GCN architecture is used, where the sample nodes of endocardial and epicardial contours are connected as a graph to represent the myocardial geometry. We show that the ST-GCN can automatically quantify the spatio-temporal patterns in cine MR that characterise cardiac motion. Experiments are performed on healthy volunteers from the UK Biobank dataset. We compare different strategies for constructing cardiac structure graphs. Experiments show that the proposed methods perform well in estimating endocardial radii and characterising cardiac motion features for regional LV analysis.

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
Pages56-65
Number of pages10
ISBN (Print)9783030681067
DOIs
StatePublished - 2021
Externally publishedYes
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
Period4/10/204/10/20

Keywords

  • Cardiac MR
  • Endocardium
  • Epicardium
  • Motion-characteristic features
  • Spatio-temporal graph convolutional networks

Fingerprint

Dive into the research topics of 'Modelling Cardiac Motion via Spatio-Temporal Graph Convolutional Networks to Boost the Diagnosis of Heart Conditions'. Together they form a unique fingerprint.

Cite this