Segmentation par coupes de graphe avec a priori de forme Application à l'IRM cardiaque
Identifieur interne : 000264 ( Main/Exploration ); précédent : 000263; suivant : 000265Segmentation par coupes de graphe avec a priori de forme Application à l'IRM cardiaque
Auteurs : Damien Grosgeorge [France]Source :
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Abstract
Segmenting the cardiac ventricles on MR Images is required for cardiac function assessment. This task is difficult, especially for the right ventricle (RV), due to the fuzziness of the boundaries of the cavities, intensity irregularities and its complex and variable shape. This is probably one of the reasons why RV functional assessment has long been considered secondary compared to that of the left ventricle (LV), leaving the problem of RV segmentation wide open. For this purpose, we proposed and developed two segmentation methods based on graphcuts (GC), in which we have incorporated a shape prior. The first method, semi-automatic, is based on a statistical prior map build from a Principal Component Analysis, integrated in the GC. The second, automatic, enables multi-object segmentation from a probabilistic shape model based on the registration and the fusion of atlases. These methods have been evaluated on a large database of cardiac MRI, consisting of 48 patients. We have compared our methods with the state of the art methods for this application through the RV segmentation challenge MICCAI'12 we organized and have shown the effectiveness of our methods.
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This task is difficult, especially for the right ventricle (RV), due to the fuzziness of the boundaries of the cavities, intensity irregularities and its complex and variable shape. This is probably one of the reasons why RV functional assessment has long been considered secondary compared to that of the left ventricle (LV), leaving the problem of RV segmentation wide open. For this purpose, we proposed and developed two segmentation methods based on graphcuts (GC), in which we have incorporated a shape prior. The first method, semi-automatic, is based on a statistical prior map build from a Principal Component Analysis, integrated in the GC. The second, automatic, enables multi-object segmentation from a probabilistic shape model based on the registration and the fusion of atlases. These methods have been evaluated on a large database of cardiac MRI, consisting of 48 patients. We have compared our methods with the state of the art methods for this application through the RV segmentation challenge MICCAI'12 we organized and have shown the effectiveness of our methods.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Haute-Normandie</li><li>Région Normandie</li></region><settlement><li>Le Havre</li><li>Rouen</li></settlement><orgName><li>Université de Rouen</li><li>Université du Havre</li></orgName></list><tree><country name="France"><region name="Région Normandie"><name sortKey="Grosgeorge, Damien" sort="Grosgeorge, Damien" uniqKey="Grosgeorge D" first="Damien" last="Grosgeorge">Damien Grosgeorge</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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