Uncertainty-Based Segmentation of Myocardial Infarction Areas on Cardiac MR Images

Robin Camarasa*, Alexis Faure, Thomas Crozier, Daniel Bos, Marleen de Bruijne

*Corresponding author for this work

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

2 Citations (Scopus)

Abstract

Every segmentation task is uncertain due to image resolution, artefacts, annotation protocol etc. Propagating those uncertainties in a segmentation pipeline can improve the segmentation. This article aims to assess if segmentation can benefit from uncertainty of an auxiliary unsupervised task - the reconstruction of the input image. This auxillary task could help the network focus on rare examples that are otherwise poorly segmented. The method was applied to segmentation of myocardial infarction areas on cardiac magnetic resonance images.

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+Business Media
Pages385-391
Number of pages7
Volume12592
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

SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12592 LNCS
ISSN0302-9743

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

Bibliographical note

Funding Information:
This work was partly funded by Netherlands Organisation for Scientific Research (NWO) VICI project VI.C.182.042.

Publisher Copyright: © 2021, Springer Nature Switzerland AG.

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