Left Ventricle Segmentation in Cardiac MR: A Systematic Mapping of the Past Decade

Left ventricle segmentation in short-axis cardiac magnetic resonance images is important to diagnose heart disease. However, repetitive manual segmentation of these images requires considerable human effort and can decrease diagnostic accuracy. In recent years, several fully and semi-automatic appro...

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Published inACM computing surveys Vol. 54; no. 11s; pp. 1 - 38
Main Authors Ribeiro, Matheus A. O., Nunes, Fátima L. S.
Format Journal Article
LanguageEnglish
Published New York, NY ACM 31.01.2022
Subjects
Applied computing
Artificial intelligence
Computing methodologies
General and reference
Image segmentation
Imaging
Surveys and overviews
cardiac magnetic resonance imaging
Left ventricle
Online AccessGet full text
ISSN0360-0300
1557-7341
1557-7341
DOI10.1145/3517190

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Abstract Left ventricle segmentation in short-axis cardiac magnetic resonance images is important to diagnose heart disease. However, repetitive manual segmentation of these images requires considerable human effort and can decrease diagnostic accuracy. In recent years, several fully and semi-automatic approaches have been proposed, mainly using image-based, atlas, graph, deformable model, and artificial intelligence methods. This article presents a systematic mapping on left ventricle segmentation, considering 74 studies published in the past decade. The main contributions of this review are definition of the main segmentation challenges in these images; proposal of a new schematization, dividing the segmentation process into stages; categorization and analysis of the segmentation methods, including hybrid combinations; and analysis of the evaluation process, metrics, and databases. The performance of the methods in the most used public database is assessed, and the main limitations, weaknesses, and strengths of each method category are presented. Finally, trends, challenges, and research opportunities are discussed. The analysis indicates that methods from all categories can achieve good performance, and hybrid methods combining deep learning and deformable models obtain the best results. Methods still fail in specific slices, segment wrong regions, and produce anatomically impossible segmentations.
AbstractList Left ventricle segmentation in short-axis cardiac magnetic resonance images is important to diagnose heart disease. However, repetitive manual segmentation of these images requires considerable human effort and can decrease diagnostic accuracy. In recent years, several fully and semi-automatic approaches have been proposed, mainly using image-based, atlas, graph, deformable model, and artificial intelligence methods. This article presents a systematic mapping on left ventricle segmentation, considering 74 studies published in the past decade. The main contributions of this review are definition of the main segmentation challenges in these images; proposal of a new schematization, dividing the segmentation process into stages; categorization and analysis of the segmentation methods, including hybrid combinations; and analysis of the evaluation process, metrics, and databases. The performance of the methods in the most used public database is assessed, and the main limitations, weaknesses, and strengths of each method category are presented. Finally, trends, challenges, and research opportunities are discussed. The analysis indicates that methods from all categories can achieve good performance, and hybrid methods combining deep learning and deformable models obtain the best results. Methods still fail in specific slices, segment wrong regions, and produce anatomically impossible segmentations.
ArticleNumber 241
Author Ribeiro, Matheus A. O.
Nunes, Fátima L. S.
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  organization: Universidade de São Paulo, São Paulo, Brazil
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Snippet Left ventricle segmentation in short-axis cardiac magnetic resonance images is important to diagnose heart disease. However, repetitive manual segmentation of...
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acm
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SubjectTerms Applied computing
Artificial intelligence
Computing methodologies
General and reference
Image segmentation
Imaging
Surveys and overviews
SubjectTermsDisplay Applied computing -- Imaging
Computing methodologies -- Artificial intelligence
Computing methodologies -- Image segmentation
General and reference -- Surveys and overviews
Title Left Ventricle Segmentation in Cardiac MR: A Systematic Mapping of the Past Decade
URI https://dl.acm.org/doi/10.1145/3517190
https://dl.acm.org/doi/pdf/10.1145/3517190
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