Paper Info
Title
Supervised learning modelization and segmentation of cardiac scar in delayed enhanced MRI
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.
Year
DOI
Venue
2012
10.1007/978-3-642-36961-2_7
STACOM
Keywords
Field
DocType
segmentation method,level set segmentation,support vector machines classifier,delayed enhanced mri,scar tissue,automated segmentation,scarified tissue,accurate segmentation,cardiac scar detection,non-viable myocardium,level set,supervised learning modelization
Computer vision,Segmentation,Support vector machine,Level set,Level set segmentation,Supervised learning,Artificial intelligence,Classifier (linguistics),Medicine,Magnetic resonance imaging
Conference
Citations 
PageRank 
References 
0
0.34
4
Authors
17
Name
Order
Citations
PageRank
Laura Lara100.34
Sergio Vera263.16
Frederic Perez300.34
Nico Lanconelli482.96
Rita Morisi542.24
Bruno Donini600.34
Dario Turco721.32
Cristiana Corsi84914.59
Claudio Lamberti9485.25
Giovana Gavidia1000.34
Maurizio Bordone1131.52
Eduardo Soudah1232.19
Nick Curzen13513.93
James A Rosengarten14513.80
John M. Morgan15605.81
Javier Herrero1600.34
Miguel A González Ballester17956.24