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<publisher>HAL CCSD</publisher>
<title lang=it>Right ventricle segmentation from cardiac MRI: a collation study.</title>
<creator>Petitjean, Caroline</creator>
<creator>Zuluaga, Maria A</creator>
<creator>Bai, Wenjia</creator>
<creator>Dacher, Jean-Nicolas</creator>
<creator>Grosgeorge, Damien</creator>
<creator>Caudron, Jérôme</creator>
<creator>Ruan, Su</creator>
<creator>Ayed, Ismail Ben</creator>
<creator>Cardoso, M Jorge</creator>
<creator>Chen, Hsiang-Chou</creator>
<creator>Jimenez-Carretero, Daniel</creator>
<creator>Ledesma-Carbayo, Maria J</creator>
<creator>Davatzikos, Christos</creator>
<creator>Doshi, Jimit</creator>
<creator>Erus, Guray</creator>
<creator>Maier, Oskar M O</creator>
<creator>Nambakhsh, Cyrus M S</creator>
<creator>Ou, Yangming</creator>
<creator>Ourselin, Sébastien</creator>
<creator>Peng, Chun-Wei</creator>
<creator>Peters, Nicholas S</creator>
<creator>Peters, Terry M</creator>
<creator>Rajchl, Martin</creator>
<creator>Rueckert, Daniel</creator>
<creator>Santos, Andres</creator>
<creator>Shi, Wenzhe</creator>
<creator>Wang, Ching-Wei</creator>
<creator>Wang, Haiyan</creator>
<creator>Yuan, Jing</creator>
<contributor>Ecologie des forêts de Guyane (ECOFOG) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD) - Institut National de la Recherche Agronomique (INRA) - Université des Antilles et de la Guyane (UAG) - AgroParisTech - Université de Guyane (UG) - Centre National de la Recherche Scientifique (CNRS)</contributor>
<contributor>Equipe Quantification en Imagerie Fonctionnelle (QuantIF-LITIS) ; Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS) ; Université Le Havre Normandie (ULH) ; Normandie Université (NU) - Normandie Université (NU) - Université de Rouen Normandie (URN) ; Normandie Université (NU) - Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie) ; Normandie Université (NU) - Université Le Havre Normandie (ULH) ; Normandie Université (NU) - Normandie Université (NU) - Université de Rouen Normandie (URN) ; Normandie Université (NU) - Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie) ; Normandie Université (NU)</contributor>
<contributor>Seccion Fisiologia y Nutricion ; Universidad de la República</contributor>
<contributor>Department of Computing [London] ; Biomedical Image Analysis Group [London] (BioMedIA) ; Imperial College London - Imperial College London</contributor>
<contributor>Service d'imagerie médicale ; Hôpital Charles Nicolle - CHU Rouen</contributor>
<contributor>Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS) ; Université Le Havre Normandie (ULH) ; Normandie Université (NU) - Normandie Université (NU) - Université de Rouen Normandie (URN) ; Normandie Université (NU) - Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie) ; Normandie Université (NU)</contributor>
<contributor>BRU-UNIDE ; Instituto Superior Técnico, Universidade Técnica de Lisboa (IST)</contributor>
<contributor>National Tsin Hua University ; Department of Electrical Engineering</contributor>
<contributor>Biomedical Image Technologies, Biomedical Research Center (CIBER-BBN) ; Universidad Politécnica de Madrid (UPM)</contributor>
<contributor>Section for Biomedical Image Analysis (SBIA) ; Department of Radiology, University of Pennsylvania</contributor>
<contributor>Department of Electrical & Computer Engineering, University of Western Ontario, London, ONT. N6A 5B9, Canada ; Department of Electrical and Computer Engineering ; University of Western Ontario (UWO) - University of Western Ontario (UWO)</contributor>
<contributor>Laboratoire Angevin de REcherche en MAthématiques (LAREMA) ; Université d'Angers (UA) - Centre National de la Recherche Scientifique (CNRS)</contributor>
<contributor>Centre for Medical Image Computing (CMIC) ; University College of London [London] (UCL)</contributor>
<contributor>Department of Biology [Utah] ; University of Utah</contributor>
<contributor>École normale supérieure - Paris (ENS Paris)</contributor>
<contributor>Université Catholique de Louvain (UCL)</contributor>
<contributor>Joint Research Laboratory on Spatial Informations ; The Hong Kong Polytechnic University [Hong Kong] (POLYU)</contributor>
<contributor>Laboratoire de Recherche en Informatique (LRI) ; Université Paris-Sud - Paris 11 (UP11) - Institut National de Recherche en Informatique et en Automatique (Inria) - CentraleSupélec - Centre National de la Recherche Scientifique (CNRS)</contributor>
<contributor>Strathclyde Institute of Pharmacy and Biomedical Science ; Strathclyde Institute of Pharmacy and Biomedical Science</contributor>
<description>International audience</description>
<source>ISSN: 1361-8415</source>
<source>Medical Image Analysis</source>
<publisher>Elsevier</publisher>
<identifier>hal-01141170</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01141170</identifier>
<source>https://hal.archives-ouvertes.fr/hal-01141170</source>
<source>Medical Image Analysis, Elsevier, 2015, 19 (1), pp.187-202</source>
<identifier>PUBMED : 25461337</identifier>
<relation>info:eu-repo/semantics/altIdentifier/pmid/25461337</relation>
<language>en</language>
<subject>[INFO] Computer Science [cs]</subject>
<subject>[SDV] Life Sciences [q-bio]</subject>
<type>info:eu-repo/semantics/article</type>
<type>Journal articles</type>
<description lang=en>Magnetic Resonance Imaging (MRI), a reference examination for cardiac morphology and function in humans, allows to image the cardiac right ventricle (RV) with high spatial resolution. The segmentation of the RV is a difficult task due to the variable shape of the RV and its ill-defined borders in these images. The aim of this paper is to evaluate several RV segmentation algorithms on common data. More precisely, we report here the results of the Right Ventricle Segmentation Challenge (RVSC), concretized during the MICCAI'12 Conference with an on-site competition. Seven automated and semi-automated methods have been considered, along them three atlas-based methods, two prior based methods, and two prior-free, image-driven methods that make use of cardiac motion. The obtained contours were compared against a manual tracing by an expert cardiac radiologist, taken as a reference, using Dice metric and Hausdorff distance. We herein describe the cardiac data composed of 48 patients, the evaluation protocol and the results. Best results show that an average 80% Dice accuracy and a 1cm Hausdorff distance can be expected from semi-automated algorithms for this challenging task on the datasets, and that an automated algorithm can reach similar performance, at the expense of a high computational burden. Data are now publicly available and the website remains open for new submissions (http://www.litislab.eu/rvsc/).</description>
<date>2015</date>
</dc>
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