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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.<\/jats:p>","DOI":"10.1145\/3517190","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T14:02:57Z","timestamp":1645711377000},"page":"1-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Left Ventricle Segmentation in Cardiac MR: A Systematic Mapping of the Past Decade"],"prefix":"10.1145","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9133-0683","authenticated-orcid":false,"given":"Matheus A. O.","family":"Ribeiro","sequence":"first","affiliation":[{"name":"Universidade de S\u00e3o Paulo, S\u00e3o Paulo, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0040-0752","authenticated-orcid":false,"given":"F\u00e1tima L. 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