Supervised learning modelization and segmentation of cardiac scar in delayed enhanced MRI

Authors:
Laura Lara;Sergio Vera;Frederic Perez;Nico Lanconelli;Rita Morisi;Bruno Donini;Dario Turco;Cristiana Corsi;Claudio Lamberti;Giovana Gavidia;Maurizio Bordone;Eduardo Soudah;Nick Curzen;James Rosengarten;John Morgan;Javier Herrero;Miguel A. González Ballester
Affiliations:
Alma IT Systems, Barcelona, Spain;Alma IT Systems, Barcelona, Spain;Alma IT Systems, Barcelona, Spain;Alma Mater Studiorum, University of Bologna, Italy;Alma Mater Studiorum, University of Bologna, Italy;Alma Mater Studiorum, University of Bologna, Italy;Alma Mater Studiorum, University of Bologna, Italy;Alma Mater Studiorum, University of Bologna, Italy;Alma Mater Studiorum, University of Bologna, Italy;Centre Internacional de Mètodes Numèrics en Enginyeria, Barcelona, Spain;Centre Internacional de Mètodes Numèrics en Enginyeria, Barcelona, Spain;Centre Internacional de Mètodes Numèrics en Enginyeria, Barcelona, Spain;University Hospital, Southampton, UK;University Hospital, Southampton, UK;University Hospital, Southampton, UK;Alma IT Systems, Barcelona, Spain;Alma IT Systems, Barcelona, Spain
Venue:
STACOM'12 Proceedings of the third international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
Year:
2012

Citing 4
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Handbook of medical imaging

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Abstract

Delayed Enhancement Magnetic Resonance Imaging can be used to non-invasively differentiate viable from non-viable myocardium within the Left Ventricle in patients suffering from myocardial diseases. Automated segmentation of scarified tissue can be used to accurately quantify the percentage of myocardium affected. This paper presents a method for cardiac scar detection and segmentation based on supervised learning and level set segmentation. First, a model of the appearance of scar tissue is trained using a Support Vector Machines classifier on image-derived descriptors. Based on the areas detected by the classifier, an accurate segmentation is performed using a segmentation method based on level sets.