Chapter title |
Segmentation of the Aortic Valve Apparatus in 3D Echocardiographic Images: Deformable Modeling of a Branching Medial Structure
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Chapter number | 20 |
Book title |
Statistical Atlases and Computational Models of the Heart - Imaging and Modelling Challenges
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Published in |
Statistical atlases and computational models of the heart : Imaging and modelling challenges : 5th International Workshop, STACOM 2014, held in conjunction with MICCAI 2014, Boston, MA, USA, September 18, 2014, Revised selected papers /..., September 2014
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DOI | 10.1007/978-3-319-14678-2_20 |
Pubmed ID | |
Book ISBNs |
978-3-31-914677-5, 978-3-31-914678-2
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Authors |
Alison M. Pouch, Sijie Tian, Manabu Takabe, Hongzhi Wang, Jiefu Yuan, Albert T. Cheung, Benjamin M. Jackson, Joseph H. GormanIII, Robert C. Gorman, Paul A. Yushkevich, Joseph H. Gorman |
Abstract |
3D echocardiographic (3DE) imaging is a useful tool for assessing the complex geometry of the aortic valve apparatus. Segmentation of this structure in 3DE images is a challenging task that benefits from shape-guided deformable modeling methods, which enable inter-subject statistical shape comparison. Prior work demonstrates the efficacy of using continuous medial representation (cm-rep) as a shape descriptor for valve leaflets. However, its application to the entire aortic valve apparatus is limited since the structure has a branching medial geometry that cannot be explicitly parameterized in the original cm-rep framework. In this work, we show that the aortic valve apparatus can be accurately segmented using a new branching medial modeling paradigm. The segmentation method achieves a mean boundary displacement of 0.6 ± 0.1 mm (approximately one voxel) relative to manual segmentation on 11 3DE images of normal open aortic valves. This study demonstrates a promising approach for quantitative 3DE analysis of aortic valve morphology. |
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