Title
Coupled deformable models with spatially varying features for quantitative assessment of left ventricular function from cardiac MRI
Abstract
Cardiac MRI has improved the diagnosis of cardiovascular diseases by enabling the quantitative assessment of functional parameters. This requires an accurate identification of the myocardium of the left ventricle. This paper describes a novel segmentation technique for automated delineation of the myocardium. We propose to use prior knowledge by integrating a statistical shape model and a spatially varying feature model into a deformable mesh adaptation framework. Our shape model consists of a coupled, layered triangular mesh of the epi- and endocardium. It is adapted to the image by iteratively carrying out i) a surface detection and ii) a mesh reconfiguration by energy minimization. For surface detection a feature search is performed to find the point with the best feature combination. To accommodate the different tissue types the triangles of the mesh are labeled, resulting in a spatially varying feature model. The energy function consists of two terms: an external energy term, which attracts the triangles towards the features, and an internal energy term. which preserves the shape of the mesh. We applied our method to 40 cardiac MRI data sets (FFE-EPI) and compared the results to manual segmentations. A mean distance of about 3 mm with a standard deviation of 2 mm to the manual segmentations was achieved.
Year
DOI
Venue
2003
10.1117/12.481352
Proceedings of SPIE
Keywords
Field
DocType
segmentation,modeling,cardiac MRI
Computer vision,Data set,Computer science,Segmentation,Feature model,Artificial intelligence,Quantitative assessment,Standard deviation,Control reconfiguration,Triangle mesh,Energy minimization
Conference
Volume
ISSN
Citations 
5032
0277-786X
1
PageRank 
References 
Authors
0.39
0
9
Name
Order
Citations
PageRank
Kirsten Meetz1479.02
Jens Von Berg224727.11
Thomas Netsch39316.47
Vladimir Pekar426124.85
steven lobregt510.73
roel truyen621.44
m j a siers710.39
Wiro J. Niessen83821380.78
Michael R. Kaus91009.41