Regression-based label fusion for multi-atlas segmentation

Authors:
Hongzhi Wang; Jung Wook Suh;S. Das;J. Pluta;M. Altinay;P. Yushkevich
Affiliations:
Dept. of Radiol., Univ. of Pennsylvania, Philadelphia, PA, USA;Dept. of Radiol., Univ. of Pennsylvania, Philadelphia, PA, USA;Dept. of Radiol., Univ. of Pennsylvania, Philadelphia, PA, USA;Dept. of Radiol., Univ. of Pennsylvania, Philadelphia, PA, USA;Dept. of Radiol., Univ. of Pennsylvania, Philadelphia, PA, USA;Dept. of Radiol., Univ. of Pennsylvania, Philadelphia, PA, USA
Venue:
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Year:
2011

Citing 0
Cited 2

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Abstract

Automatic segmentation using multi-atlas label fusion has been widely applied in medical image analysis. To simplify the label fusion problem, most methods implicitly make a strong assumption that the segmentation errors produced by different atlases are uncorrelated. We show that violating this assumption significantly reduces the efficiency of multi-atlas segmentation. To address this problem, we propose a regression-based approach for label fusion. Our experiments on segmenting the hippocampus in magnetic resonance images (MRI) show significant improvement over previous label fusion techniques.