Learning a global descriptor of cardiac motion from a large cohort of 1000+ normal subjects

Wenjia Bai*, Devis Peressutti, Ozan Oktay, Wenzhe Shi, Declan P. O’Regan, Andrew P. King, Daniel Rueckert

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

Motion, together with shape, reflect important aspects of cardiac function. In this work, a new method is proposed for learning of a cardiac motion descriptor from a data-driven perspective. The resulting descriptor can characterise the global motion pattern of the left ventricle with a much lower dimension than the original motion data. It has demonstrated its predictive power on two exemplar classification tasks on a large cohort of 1093 normal subjects.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer‐Verlag Berlin Heidelberg
Pages3-11
Number of pages9
Volume9126
ISBN (Print)9783319203089, 9783319203089
DOIs
Publication statusPublished - 2015
Event8th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2015 - Maastricht, Netherlands
Duration: 25 Jun 201527 Jun 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9126
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference8th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2015
Country/TerritoryNetherlands
CityMaastricht
Period25/06/201527/06/2015

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