LV Challenge LKEB Contribution: Fully Automated Myocardial Contour Detection
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3115
In this paper a contour detection method is described and evaluated
on the evaluation data sets of the Cardiac MR Left Ventricle Segmentation
Challenge as part of MICCAI 2009’s 3D Segmentation Challenge for Clinical
Applications. The proposed method, using 2D AAM and 3D ASM, performs a
fully automated detection of the myocardial contours, not requiring any user
interaction. The algorithm’s performance is reported using the metrics provided
by the LV Challenge organization. Endocardial contour detection was classified
as successful in 86% of the images and epicardial contours in 94%. The average
perpendicular distance (APD) of the successful contours was 2.28 mm and
2.29 mm for the endo- and epicardial contours, respectively.

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Categories: Active appearance models, Classification, Feature extraction, Iterative clustering, Point distribution models, Segmentation, Statistical shape models, Unsupervised learning and clustering
Keywords: segmentation, cardiac, mri, left ventricle, aam, asm
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