Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images

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• dc.contributor.author Karim, Rashed
• dc.contributor.author Karim, Rashed
• dc.contributor.author Albà, Xènia
• dc.contributor.author González Ballester, Miguel Ángel, 1973-
• dc.contributor.author Rhode, Kawal
• dc.date.accessioned 2024-01-29T07:21:58Z
• dc.date.available 2024-01-29T07:21:58Z
• dc.date.issued 2016
• dc.description.abstract Studies have demonstrated the feasibility of late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging for guiding the management of patients with sequelae to myocardial infarction, such as ventricular tachycardia and heart failure. Clinical implementation of these developments necessitates a reproducible and reliable segmentation of the infarcted regions. It is challenging to compare new algorithms for infarct segmentation in the left ventricle (LV) with existing algorithms. Benchmarking datasets with evaluation strategies are much needed to facilitate comparison. This manuscript presents a benchmarking evaluation framework for future algorithms that segment infarct from LGE CMR of the LV. The image database consists of 30 LGE CMR images of both humans and pigs that were acquired from two separate imaging centres. A consensus ground truth was obtained for all data using maximum likelihood estimation. Six widely-used fixed-thresholding methods and five recently developed algorithms are tested on the benchmarking framework. Results demonstrate that the algorithms have better overlap with the consensus ground truth than most of the n-SD fixed-thresholding methods, with the exception of the Full-Width-at-Half-Maximum (FWHM) fixed-thresholding method. Some of the pitfalls of fixed thresholding methods are demonstrated in this work. The benchmarking evaluation framework, which is a contribution of this work, can be used to test and benchmark future algorithms that detect and quantify infarct in LGE CMR images of the LV. The datasets, ground truth and evaluation code have been made publicly available through the website: https://www.cardiacatlas.org/web/guest/challenges.
• dc.description.sponsorship The author acknowledges support from the King’s College London Centre of Excellence in Medical Engineering funded by the Wellcome Trust and EPSRC (WT 088641/Z/09/Z). This research was also supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. Miguel A. Gonzalez Ballester was partially funded by project TIN2013-47913-C3-1-R, from the Spanish Ministry of Economy and Competitiveness.
• dc.format.mimetype application/pdf
• dc.identifier.citation Karim R, Bhagirath P, Claus P, James Housden R, Chen Z, Karimaghaloo Z, et al. Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images. Medical Image Analysis. 2016 May;30:95-107. DOI: 10.1016/j.media.2016.01.004
• dc.identifier.doi http://dx.doi.org/10.1016/j.media.2016.01.004
• dc.identifier.issn 1361-8415
• dc.identifier.uri http://hdl.handle.net/10230/58849
• dc.language.iso eng
• dc.publisher Elsevier
• dc.relation.ispartof Medical Image Analysis. 2016 May;30:95-107
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2013-47913-C3-1-R
• dc.rights © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Late Gadolinium enhancement
• dc.subject.keyword Segmentation
• dc.subject.keyword Algorithm benchmarking
• dc.title Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion