{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T06:57:51Z","timestamp":1773385071856,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,8,20]],"date-time":"2021-08-20T00:00:00Z","timestamp":1629417600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Late gadolinium enhancement (LGE) MRI is the gold standard technique for myocardial viability assessment. Although the technique accurately reflects the damaged tissue, there is no clinical standard to quantify myocardial infarction (MI). Moreover, commercial software used in clinical practice are mostly semi-automatic, and hence require direct intervention of experts. In this work, a new automatic method for MI quantification from LGE-MRI is proposed. Our novel segmentation approach is devised for accurately detecting not only hyper-enhanced lesions, but also microvascular obstruction areas. Moreover, it includes a myocardial disease detection step which extends the algorithm for working under healthy scans. The method is based on a cascade approach where firstly, diseased slices are identified by a convolutional neural network (CNN). Secondly, by means of morphological operations a fast coarse scar segmentation is obtained. Thirdly, the segmentation is refined by a boundary-voxel reclassification strategy using an ensemble of very light CNNs. We tested the method on a LGE-MRI database with healthy (n = 20) and diseased (n = 80) cases following a 5-fold cross-validation scheme. Our approach segmented myocardial scars with an average Dice coefficient of 77.22 \u00b1 14.3% and with a volumetric error of 1.0 \u00b1 6.9 cm3. In a comparison against nine reference algorithms, the proposed method achieved the highest agreement in volumetric scar quantification with the expert delineations (p&lt; 0.001 when compared to the other approaches). Moreover, it was able to reproduce the scar segmentation intra- and inter-rater variability. Our approach was shown to be a good first attempt towards automatic and accurate myocardial scar segmentation, although validation over larger LGE-MRI databases is needed.<\/jats:p>","DOI":"10.3390\/a14080249","type":"journal-article","created":{"date-parts":[[2021,8,22]],"date-time":"2021-08-22T21:42:12Z","timestamp":1629668532000},"page":"249","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Myocardial Infarction Quantification from Late Gadolinium Enhancement MRI Using Top-Hat Transforms and Neural Networks"],"prefix":"10.3390","volume":"14","author":[{"given":"Ezequiel","family":"de la Rosa","sequence":"first","affiliation":[{"name":"ImViA, University of Burgundy, 21000 Dijon, France"}]},{"given":"D\u00e9sir\u00e9","family":"Sidib\u00e9","sequence":"additional","affiliation":[{"name":"ImViA, University of Burgundy, 21000 Dijon, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6794-5306","authenticated-orcid":false,"given":"Thomas","family":"Decourselle","sequence":"additional","affiliation":[{"name":"Casis Company, 21800 Quetigny, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9333-3896","authenticated-orcid":false,"given":"Thibault","family":"Leclercq","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital of Dijon, 21000 Dijon, France"}]},{"given":"Alexandre","family":"Cochet","sequence":"additional","affiliation":[{"name":"ImViA, University of Burgundy, 21000 Dijon, France"},{"name":"Department of Radiology, University Hospital of Dijon, 21000 Dijon, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7970-366X","authenticated-orcid":false,"given":"Alain","family":"Lalande","sequence":"additional","affiliation":[{"name":"ImViA, University of Burgundy, 21000 Dijon, France"},{"name":"Department of Radiology, University Hospital of Dijon, 21000 Dijon, France"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1136\/heartjnl-2014-306963","article-title":"MRI in the assessment of ischaemic heart disease","volume":"102","author":"Dastidar","year":"2016","journal-title":"Heart"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1186\/s12968-018-0484-5","article-title":"Society for Cardiovascular Magnetic Resonance (SCMR) expert consensus for CMR imaging endpoints in clinical research: PartI-analytical validation and clinical qualification","volume":"20","author":"Puntmann","year":"2018","journal-title":"J. Cardiovasc. Magn. Reson."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1148\/rg.335125722","article-title":"MR imaging of myocardial infarction","volume":"33","author":"Rajiah","year":"2013","journal-title":"Radiographics"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1992","DOI":"10.1161\/01.CIR.100.19.1992","article-title":"Relationship of MRI delayed contrast enhancement to irreversible injury, infarct age, and contractile function","volume":"100","author":"Kim","year":"1999","journal-title":"Circulation"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.1007\/s00330-009-1395-5","article-title":"Major prognostic impact of persistent microvascular obstruction as assessed by contrast-enhanced cardiac magnetic resonance in reperfused acute myocardial infarction","volume":"19","author":"Cochet","year":"2009","journal-title":"Eur. Radiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1016\/j.jcmg.2014.06.012","article-title":"Effect of microvascular obstruction and intramyocardial hemorrhage by CMR on LV remodeling and outcomes after myocardial infarction: A systematic review and meta-analysis","volume":"7","author":"Hamirani","year":"2014","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"20140470","DOI":"10.1259\/bjr.20140470","article-title":"Cardiac MR assessment of microvascular obstruction","volume":"88","author":"Abbas","year":"2015","journal-title":"Br. J. Radiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s12968-016-0242-5","article-title":"A new automatic algorithm for quantification of myocardial infarction imaged by late gadolinium enhancement cardiovascular magnetic resonance: Experimental validation and comparison to expert delineations in multi-center, multi-vendor patient data","volume":"18","author":"Engblom","year":"2016","journal-title":"J. Cardiovasc. Magn. Reson."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/j.rcl.2014.11.005","article-title":"Tissue characterization of the myocardium: State of the art characterization by magnetic resonance and computed tomography imaging","volume":"53","author":"Pattanayak","year":"2015","journal-title":"Radiol. Clin."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2383","DOI":"10.1016\/j.jacc.2004.09.020","article-title":"Accurate and objective infarct sizing by contrast-enhanced magnetic resonance imaging in a canine myocardial infarction model","volume":"44","author":"Amado","year":"2004","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.jcmg.2010.11.015","article-title":"Evaluation of techniques for the quantification of myocardial scar of differing etiology using cardiac magnetic resonance","volume":"4","author":"Flett","year":"2011","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e149","DOI":"10.1016\/j.ejrad.2009.05.035","article-title":"Comparison of different quantification methods of late gadolinium enhancement in patients with hypertrophic cardiomyopathy","volume":"74","author":"Spiewak","year":"2010","journal-title":"Eur. J. Radiol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1002\/jmri.25285","article-title":"Myocardial infarct sizing by late gadolinium-enhanced MRI: Comparison of manual, full-width at half-maximum, and n-standard deviation methods","volume":"44","author":"Zhang","year":"2016","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Karim, R., Claus, P., Chen, Z., Housden, R.J., Obom, S., Gill, H., Ma, Y., Acheampong, P., O\u2019Neill, M., and Razavi, R. (2012). Infarct segmentation challenge on delayed enhancement MRI of the left ventricle. International Workshop on Statistical Atlases and Computational Models of the Heart, Springer.","DOI":"10.1007\/978-3-642-36961-2_12"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1592","DOI":"10.1109\/TMI.2008.2006512","article-title":"A comprehensive approach to the analysis of contrast enhanced cardiac MR images","volume":"27","author":"Hennemuth","year":"2008","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1002\/mrm.22422","article-title":"Automated segmentation of myocardial scar in late enhancement MRI using combined intensity and spatial information","volume":"64","author":"Tao","year":"2010","journal-title":"Magn. Reson. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1109\/TBME.2013.2237907","article-title":"A comprehensive 3-D framework for automatic quantification of late gadolinium enhanced cardiac magnetic resonance images","volume":"60","author":"Wei","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1097\/RTI.0000000000000206","article-title":"Comparison of image processing techniques for nonviable tissue quantification in late gadolinium enhancement cardiac magnetic resonance images","volume":"31","author":"Carminati","year":"2016","journal-title":"J. Thorac. Imaging"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5009","DOI":"10.1088\/0031-9155\/58\/15\/5009","article-title":"Quantification of fibrosis in infarcted swine hearts by late gadolinium-enhancement and diffusion-weighted MRI methods","volume":"58","author":"Pop","year":"2013","journal-title":"Phys. Med. Biol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Valindria, V.V., Angue, M., Vignon, N., Walker, P.M., Cochet, A., and Lalande, A. (December, January 28). Automatic quantification of myocardial infarction from delayed enhancement MRI. Proceedings of the 2011 Seventh International Conference on Signal-Image Technology and Internet-Based Systems (SITIS), Dijon, France.","DOI":"10.1109\/SITIS.2011.83"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.media.2017.11.008","article-title":"Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis","volume":"44","author":"Zreik","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s10554-004-5806-z","article-title":"Segmentation of non-viable myocardium in delayed enhancement magnetic resonance images","volume":"21","author":"Kolipaka","year":"2005","journal-title":"Int. J. Cardiovasc. Imaging"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man, Cybern."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1161\/CIRCEP.111.961946","article-title":"Integration of 3D electroanatomic maps and magnetic resonance scar characterization into the navigation system to guide ventricular tachycardia ablation clinical perspective","volume":"4","author":"Andreu","year":"2011","journal-title":"Circ. Arrhythmia Electrophysiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2006","DOI":"10.1161\/CIRCULATIONAHA.106.653568","article-title":"Infarct tissue heterogeneity by magnetic resonance imaging identifies enhanced cardiac arrhythmia susceptibility in patients with left ventricular dysfunction","volume":"115","author":"Schmidt","year":"2007","journal-title":"Circulation"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1002\/jmri.20496","article-title":"Quantitative myocardial infarction on delayed enhancement MRI. Part I: Animal validation of an automated feature analysis and combined thresholding infarct sizing algorithm","volume":"23","author":"Hsu","year":"2006","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1606","DOI":"10.1109\/TMI.2009.2023515","article-title":"Reproducible classification of infarct heterogeneity using fuzzy clustering on multicontrast delayed enhancement magnetic resonance images","volume":"28","author":"Detsky","year":"2009","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1081\/JCMR-200053630","article-title":"A fast and effective method to assess myocardial necrosis by means of contrast magnetic resonance imaging","volume":"7","author":"Positano","year":"2005","journal-title":"J. Cardiovasc. Magn. Reson."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Dikici, E., O\u2019Donnell, T., Setser, R., and White, R.D. (2004, January 26\u201329). Quantification of delayed enhancement MR images. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Saint-malo, France.","DOI":"10.1007\/978-3-540-30135-6_31"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1117\/12.480422","article-title":"Semi-automatic segmentation of nonviable cardiac tissue using cine and delayed enhancement magnetic resonance images","volume":"Volume 5031","author":"Xu","year":"2003","journal-title":"Medical Imaging 2003: Physiology and Function: Methods, Systems, and Applications"},{"key":"ref_31","first-page":"81","article-title":"Automated quantification of myocardial infarction using graph cuts on contrast delayed enhanced magnetic resonance images","volume":"2","author":"Lu","year":"2012","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.irbm.2017.06.004","article-title":"Segmentation Integrating Watershed and Shape Priors Applied to Cardiac Delayed Enhancement MR Images","volume":"38","author":"Kruk","year":"2017","journal-title":"IRBM"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1109\/TMI.2015.2512711","article-title":"Myocardial infarct segmentation from magnetic resonance images for personalized modeling of cardiac electrophysiology","volume":"35","author":"Ukwatta","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1109\/TMI.2013.2282932","article-title":"Interactive hierarchical-flow segmentation of scar tissue from late-enhancement cardiac MR images","volume":"33","author":"Rajchl","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_35","first-page":"1","article-title":"Automated Scar Segmentation From CMR-LGE Images Using a Deep Learning Approach","volume":"Volume 45","author":"Moccia","year":"2018","journal-title":"Proceedings of the 2018 Computing in Cardiology Conference (CinC)"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s10334-018-0718-4","article-title":"Development and testing of a deep learning-based strategy for scar segmentation on CMR-LGE images","volume":"32","author":"Moccia","year":"2019","journal-title":"Magn. Reson. Mater. Phys. Biol. Med."},{"key":"ref_37","first-page":"663","article-title":"Myocardial scar segmentation from magnetic resonance images using convolutional neural network","volume":"Volume 10575","author":"Petrick","year":"2018","journal-title":"Medical Imaging 2018: Computer-Aided Diagnosis"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1740","DOI":"10.1002\/mp.13436","article-title":"Convolutional neural network-based approach for segmentation of left ventricle myocardial scar from 3D late gadolinium enhancement MR images","volume":"46","author":"Zabihollahy","year":"2019","journal-title":"Med. Phys."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yang, G., Zhuang, X., Khan, H., Haldar, S., Nyktari, E., Ye, X., Slabaugh, G., Wong, T., Mohiaddin, R., and Keegan, J. (2017, January 18\u201321). A fully automatic deep learning method for atrial scarring segmentation from late gadolinium-enhanced MRI images. Proceedings of the 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, Australia.","DOI":"10.1109\/ISBI.2017.7950649"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1562","DOI":"10.1002\/mp.12832","article-title":"Fully automatic segmentation and objective assessment of atrial scars for long-standing persistent atrial fibrillation patients using late gadolinium-enhanced MRI","volume":"45","author":"Yang","year":"2018","journal-title":"Med. Phys."},{"key":"ref_41","unstructured":"Paszke, A., Chaurasia, A., Kim, S., and Culurciello, E. (2016). ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1002\/mp.14022","article-title":"Fully automated segmentation of left ventricular scar from 3D late gadolinium enhancement magnetic resonance imaging using a cascaded multi-planar U-Net (CMPU-Net)","volume":"47","author":"Zabihollahy","year":"2020","journal-title":"Med. Phys."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Fahmy, A.S., Rowin, E.J., Chan, R.H., Manning, W.J., Maron, M.S., and Nezafat, R. (2021). Improved quantification of myocardium scar in late gadolinium enhancement images: Deep learning based image fusion approach. J. Magn. Reson. Imaging.","DOI":"10.1002\/jmri.27555"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Lalande, A., Garreau, M., and Frouin, F. (2015). Evaluation of cardiac structure segmentation in cine magnetic resonance imaging. Multi-Modality Cardiac Imaging: Processing and Analysis, Wiley Online Library.","DOI":"10.1002\/9781118574362.ch5"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lalande, A., Chen, Z., Decourselle, T., Qayyum, A., Pommier, T., Lorgis, L., de la Rosa, E., Cochet, A., Cottin, Y., and Ginhac, D. (2020). Emidec: A database usable for the automatic evaluation of myocardial infarction from delayed-enhancement cardiac MRI. Data, 5.","DOI":"10.3390\/data5040089"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1002\/jmri.22003","article-title":"Adaptive non-local means denoising of MR images with spatially varying noise levels","volume":"31","author":"Collins","year":"2010","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_47","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"5162","DOI":"10.1002\/mp.12453","article-title":"A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets","volume":"44","author":"Antropova","year":"2017","journal-title":"Med. Phys."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.1016\/j.neucom.2017.09.084","article-title":"Atlas registration and ensemble deep convolutional neural network-based prostate segmentation using magnetic resonance imaging","volume":"275","author":"Jia","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s40537-019-0192-5","article-title":"Survey on deep learning with class imbalance","volume":"6","author":"Johnson","year":"2019","journal-title":"J. Big Data"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","article-title":"Learning from class-imbalanced data: Review of methods and applications","volume":"73","author":"Haixiang","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_52","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1002\/cyto.a.22017","article-title":"Segmentation and detection of fluorescent 3D spots","volume":"81","author":"Ram","year":"2012","journal-title":"Cytom. Part A"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"BahadarKhan, K., Khaliq, A.A., and Shahid, M. (2016). A morphological hessian based approach for retinal blood vessels segmentation and denoising using region based otsu thresholding. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0158996"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Savelli, B., Marchesi, A., Bria, A., Marrocco, C., Molinara, M., and Tortorella, F. (2017, January 11\u201315). Retinal Vessel Segmentation Through Denoising and Mathematical Morphology. Proceedings of the International Conference on Image Analysis and Processing, Catania, Italy.","DOI":"10.1007\/978-3-319-68548-9_25"},{"key":"ref_56","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"89986","DOI":"10.1109\/ACCESS.2019.2926697","article-title":"Quantitative Analysis of Patch-Based Fully Convolutional Neural Networks for Tissue Segmentation on Brain Magnetic Resonance Imaging","volume":"7","author":"Bernal","year":"2019","journal-title":"IEEE Access"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.ijcard.2015.03.359","article-title":"Novel insights into an \u201cold\u201d phenomenon: The no reflow","volume":"187","author":"Durante","year":"2015","journal-title":"Int. J. Cardiol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0140-6736(86)90837-8","article-title":"Statistical methods for assessing agreement between two methods of clinical measurement","volume":"327","author":"Bland","year":"1986","journal-title":"Lancet"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016, January 25\u201328). V-net: Fully convolutional neural networks for volumetric medical image segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Mehta, S., Mercan, E., Bartlett, J., Weaver, D., Elmore, J.G., and Shapiro, L. (2018, January 16\u201320). Y-Net: Joint segmentation and classification for diagnosis of breast biopsy images. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Granada, Spain.","DOI":"10.1007\/978-3-030-00934-2_99"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.cmpb.2019.04.016","article-title":"A novel framework for MR image segmentation and quantification by using MedGA","volume":"176","author":"Rundo","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1186\/s12968-016-0308-4","article-title":"Cardiac T1 mapping and extracellular volume (ECV) in clinical practice: A comprehensive review","volume":"18","author":"Haaf","year":"2017","journal-title":"J. Cardiovasc. Magn. Reson."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Xu, C., Xu, L., Gao, Z., Zhao, S., Zhang, H., Zhang, Y., Du, X., Zhao, S., Ghista, D., and Li, S. (2017, January 11\u201313). Direct detection of pixel-level myocardial infarction areas via a deep-learning algorithm. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Quebec City, QC, Canada.","DOI":"10.1007\/978-3-319-66179-7_28"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1148\/radiol.2019182304","article-title":"Deep Learning for Diagnosis of Chronic Myocardial Infarction on Nonenhanced Cardiac Cine MRI","volume":"291","author":"Zhang","year":"2019","journal-title":"Radiology"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/8\/249\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:48:10Z","timestamp":1760165290000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/8\/249"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,20]]},"references-count":67,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["a14080249"],"URL":"https:\/\/doi.org\/10.3390\/a14080249","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,20]]}}}