Paper
15 May 2003 Coupled deformable models with spatially varying features for quantitative assessment of left ventricular function from cardiac MRI
Kirsten Meetz, Jens von Berg, Thomas Netsch, Vladimir Pekar, Steven Lobregt, Roel Truyen, Miriam Siers, Wiro J. Niessen, Michael R. Kaus
Author Affiliations +
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.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kirsten Meetz, Jens von Berg, Thomas Netsch, Vladimir Pekar, Steven Lobregt, Roel Truyen, Miriam Siers, Wiro J. Niessen, and Michael R. Kaus "Coupled deformable models with spatially varying features for quantitative assessment of left ventricular function from cardiac MRI", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.481352
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CITATIONS
Cited by 2 scholarly publications and 2 patents.
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KEYWORDS
Image segmentation

Data modeling

3D modeling

Magnetic resonance imaging

Cardiovascular magnetic resonance imaging

Natural surfaces

Statistical modeling

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