Self-correcting multi-atlas segmentation

Yi Gao; Andrew Wilford; Liang Guo

doi:10.1117/12.2217276

21 March 2016 Self-correcting multi-atlas segmentation

Yi Gao, Andrew Wilford, Liang Guo

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 97842Q (2016) https://doi.org/10.1117/12.2217276
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

Citation Download Citation

Yi Gao, Andrew Wilford, and Liang Guo "Self-correcting multi-atlas segmentation", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97842Q (21 March 2016); https://doi.org/10.1117/12.2217276

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