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The \u201cgold standard\u201d method for imaging MI is cardiovascular magnetic resonance imaging (MRI) with intravenously administered gadolinium-based contrast (with damaged areas apparent as late gadolinium enhancement [LGE]). However, no \u201cgold standard\u201d fully automated method for the quantification of MI exists. In this work, we propose an end-to-end fully automatic system (MyI-Net) for the detection and quantification of MI in MRI images. It has the potential to reduce uncertainty due to technical variability across labs and the inherent problems of data and labels. Our system consists of four processing stages designed to maintain the flow of information across scales. First, features from raw MRI images are generated using feature extractors built on ResNet and MoblieNet architectures. This is followed by atrous spatial pyramid pooling (ASPP) to produce spatial information at different scales to preserve more image context. High-level features from ASPP and initial low-level features are concatenated at the third stage and then passed to the fourth stage where spatial information is recovered via up-sampling to produce final image segmentation output into: (i) background, (ii) heart muscle, (iii) blood and (iv) LGE areas. Our experiments show that the model named MI-ResNet50-AC provides the best global accuracy (97.38%), mean accuracy (86.01%), weighted intersection over union (IoU) of 96.47%, and bfscore of 64.46% for the global segmentation. However, in detecting only LGE tissue, a smaller model, MI-ResNet18-AC, exhibited higher accuracy (74.41%) than MI-ResNet50-AC (64.29%). New models were compared with state-of-the-art models and manual quantification. Our models demonstrated favorable performance in global segmentation and LGE detection relative to the state-of-the-art, including a four-fold better performance in matching LGE pixels to contours produced by clinicians.<\/jats:p>","DOI":"10.3390\/e25030431","type":"journal-article","created":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T02:28:09Z","timestamp":1677551289000},"page":"431","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4713-2791","authenticated-orcid":false,"given":"Shuihua","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Cardiovascular Sciences, University of LeicesterGlenfield Hospital, Leicester LE3 9QP, UK"},{"name":"The NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester LE3 9QP, UK"},{"name":"School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed M. S. E. K.","family":"Abdelaty","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Sciences, University of LeicesterGlenfield Hospital, Leicester LE3 9QP, UK"},{"name":"The NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester LE3 9QP, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1842-3290","authenticated-orcid":false,"given":"Kelly","family":"Parke","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Sciences, University of LeicesterGlenfield Hospital, Leicester LE3 9QP, UK"},{"name":"The NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester LE3 9QP, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jayanth Ranjit","family":"Arnold","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Sciences, University of LeicesterGlenfield Hospital, Leicester LE3 9QP, UK"},{"name":"The NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester LE3 9QP, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerry P.","family":"McCann","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Sciences, University of LeicesterGlenfield Hospital, Leicester LE3 9QP, UK"},{"name":"The NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester LE3 9QP, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ivan Y.","family":"Tyukin","sequence":"additional","affiliation":[{"name":"Department of Mathematics, King\u2019s College London, London WC2R 2LS, UK"},{"name":"Department of Geoscience and Petroleum, Norwegian University of Science and Technology, 7491 Trondheim, Norway"},{"name":"Department of Automation and Control Processes, Saint-Petersburg State Electrotechnical University, 197022 Saint-Petersburg, Russia"},{"name":"Laboratory of Advanced Methods for High-Dimensional Data Analysis, Lobachevsky University, 603105 Nizhni Novgorod, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"87A","DOI":"10.1016\/j.chest.2018.08.078","article-title":"Traumatic rca dissection as a cause of inferior wall st elevation mi","volume":"154","author":"Tambe","year":"2018","journal-title":"Chest"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.yjmcc.2019.11.103","article-title":"Nogo-C causes post-MI arrhythmia through increasing calcium leakage from sarcoplasmic reticulum","volume":"140","author":"Jia","year":"2020","journal-title":"J. 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