Regression-Based Label Fusion for Multi-Atlas Segmentation

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.

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Hongzhi Wang, Jung Wook Suh, John Pluta, Murat Altinay, Paul Yushkevich
• Keywords: Computer Vision, CVPR 2011, Label Fusion, Label Fusion Problem, Multi-atlas Label Fusion