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Proceedings Paper

Self-correcting multi-atlas segmentation
Author(s): Yi Gao; Andrew Wilford; Liang Guo
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Paper Abstract

In multi-atlas segmentation, one typically registers several atlases to the new image, and their respective segmented label images are transformed and fused to form the final segmentation. After each registration, the quality of the registration is reflected by the single global value: the final registration cost. Ideally, if the quality of the registration can be evaluated at each point, independent of the registration process, which also provides a direction in which the deformation can further be improved, the overall segmentation performance can be improved. We propose such a self-correcting multi-atlas segmentation method. The method is applied on hippocampus segmentation from brain images and statistically significantly improvement is observed.

Paper Details

Date Published: 21 March 2016
PDF: 6 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97842Q (21 March 2016); doi: 10.1117/12.2217276
Show Author Affiliations
Yi Gao, Stony Brook Univ. (United States)
Andrew Wilford, McGill Univ. (Canada)
Liang Guo, The Ohio State Univ. (United States)

Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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