title: Regression-Based Label Fusion for Multi-Atlas Segmentation.
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.
authors.forename: Hongzhi
authors.initials: H
authors.lastname: Wang
authors.forename: Jung Wook
authors.initials: JW
authors.lastname: Suh
authors.forename: Sandhitsu
authors.initials: S
authors.lastname: Das
authors.forename: John
authors.initials: J
authors.lastname: Pluta
authors.forename: Murat
authors.initials: M
authors.lastname: Altinay
authors.forename: Paul
authors.initials: P
authors.lastname: Yushkevich