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Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?

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dc.contributor.author Bernard, Olivier
dc.contributor.author Lalande, Alain
dc.contributor.author Zotti, Clement
dc.contributor.author Cervenansky, Frederick
dc.contributor.author Yang, Xin
dc.contributor.author Heng, Pheng-Ann
dc.contributor.author Cetin, Irem
dc.contributor.author Lekadir, Karim, 1977-
dc.contributor.author Camara, Oscar
dc.contributor.author González Ballester, Miguel Ángel, 1973-
dc.contributor.author Sanromà, Gerard
dc.contributor.author Napel, Sandy
dc.contributor.author Petersen, Steffen
dc.contributor.author Tziritas, Georgios
dc.contributor.author Grinias, Elias
dc.contributor.author Khened, Mahendra
dc.contributor.author Kollerathu, Varghese Alex
dc.contributor.author Krishnamurthi, Ganapathy
dc.contributor.author Rohé, Marc-Michel
dc.contributor.author Pennec, Xavier
dc.contributor.author Sermesant, Maxime
dc.contributor.author Isensee, Fabian
dc.contributor.author Jäger, Paul
dc.contributor.author Maier-Hein, Klaus H.
dc.contributor.author Full, Peter M.
dc.contributor.author Wolf, Ivo
dc.contributor.author Engelhardt, Sandy
dc.contributor.author Baumgartner, Christian F.
dc.contributor.author Koch, Lisa M.
dc.contributor.author Wolterink, Jelmer M.
dc.contributor.author Išgum, Ivana
dc.contributor.author Jang, Yeonggul
dc.contributor.author Hong, Yoonmi
dc.contributor.author Patravali, Jay
dc.contributor.author Jain, Shubham
dc.contributor.author Humbert, Olivier
dc.contributor.author Jodoin, Pierre-Marc
dc.date.accessioned 2021-06-29T08:10:49Z
dc.date.available 2021-06-29T08:10:49Z
dc.date.issued 2018
dc.identifier.citation Bernard O, Lalande A, Zotti C, Cervenansky F, Yang X, González Ballester MA, et al. Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans Med Imaging. 2018;37(11):2514-25. DOI: 10.1109/TMI.2018.2837502
dc.identifier.issn 0278-0062
dc.identifier.uri http://hdl.handle.net/10230/48000
dc.description.abstract Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof IEEE Transactions on Medical Imaging. 2018;37(11):2514-25
dc.rights © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/TMI.2018.2837502
dc.title Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1109/TMI.2018.2837502
dc.subject.keyword Cardiac segmentation and diagnosis
dc.subject.keyword Deep learning
dc.subject.keyword MRI
dc.subject.keyword Left and right ventricles
dc.subject.keyword Myocardium
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion

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