In this work, we investigate the computation on a shape manifold for atlas generation and application to atlas propagation and segmentation. We formulate the computation of Fréchet mean via the constant velocity fields and Log-Euclidean framework for Nadaraya-Watson kernel regression modeling. In this formulation, we directly compute the Fréchet mean of shapes via fast vectorial operations on the velocity fields. By using image similarity metric to estimate the distance of shapes in the assumed manifold, we can estimate a close shape of an unseen image using Naderaya-Watson kernel regression function. We applied this estimation to generate subject-specific atlases for whole heart segmentation of MRI data. The segmentation results on clinical data demonstrated an improved performance compared to existing methods, thanks to the usage of subject-specific atlases which had more similar shapes to the unseen images.

Date Published: 13 March 2013
PDF: 7 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866941 (13 March 2013); doi: 10.1117/12.2007181

Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)