Key result
Hybrid deep learning and deformable models achieve superior LV segmentation in CMR.
Why the study?
Manual left ventricle segmentation in short-axis cardiac MR is laborious and can reduce diagnostic accuracy, prompting multiple automatic and semi-automatic methods that warrant systematic analysis.
Systematic Review (n=74)
This systematic review highlights that hybrid methods combining deep learning and deformable models yield the best performance for left ventricle segmentation in cardiac MRI.
Answers are grounded in this paper and cite their sources.
Hybrid methods may improve LV segmentation accuracy in cardiac MR; extends evidence by identifying top performers across 74 studies.
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.
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Ribeiro et al. (2022) conducted a systematic review in Left ventricle segmentation in cardiac MR (n=74). Left ventricle segmentation methods was evaluated on Segmentation performance. Hybrid methods combining deep learning and deformable models obtained the best results for left ventricle segmentation in cardiac MR among 74 reviewed studies.
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