Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-319-28712-6_2
Title: Beyond the AHA 17-segment model: Motion-driven parcellation of the left ventricle
Authors: Bai W.
Peressutti D.
Parisot S.
Oktay O.
Rajchl M.
O�Regan D.
Cook S. 
King A.
Rueckert D.
Issue Date: 2016
Publisher: Springer Verlag
Citation: Bai W., Peressutti D., Parisot S., Oktay O., Rajchl M., O�Regan D., Cook S., King A., Rueckert D. (2016). Beyond the AHA 17-segment model: Motion-driven parcellation of the left ventricle. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9534 : 13-20. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-319-28712-6_2
Abstract: A major challenge for cardiac motion analysis is the highdimensionality of the motion data. Conventionally, the AHA model is used for dimensionality reduction, which divides the left ventricle into 17 segments using criteria based on anatomical structures. In this paper, a novel method is proposed to divide the left ventricle into homogeneous parcels in terms of motion trajectories. We demonstrate that the motion-driven parcellation has good reproducibility and use it for data reduction and motion description on a dataset of 1093 subjects. The resulting motion descriptor achieves high performance on two exemplar applications, namely gender and age predictions. The proposed method has the potential to be applied to groupwise motion analysis. � Springer International Publishing Switzerland 2016.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/149248
ISSN: 3029743
DOI: 10.1007/978-3-319-28712-6_2
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