title: Optimal weights for multi-atlas label fusion.
abstract: Multi-atlas based segmentation has been applied widely in medical image analysis. For label fusion, previous studies show that image similarity-based local weighting techniques produce the most accurate results. However, these methods ignore the correlations between results produced by different atlases. Furthermore, they rely on pre-selected weighting models and ad hoc methods to choose model parameters. We propose a novel label fusion method to address these limitations. Our formulation directly aims at reducing the expectation of the combined error and can be efficiently solved in a closed form. In our hippocampus segmentation experiment, our method significantly outperforms similarity-based local weighting. Using 20 atlases, we produce results with 0.898 +/- 0.019 Dice overlap to manual labelings for controls.
authors.affiliation: PICSL, Department of Radiology, University of Pennsylvania, USA.
authors.forename: Hongzhi
authors.initials: H
authors.lastname: Wang
authors.forename: Jung Wook
authors.initials: JW
authors.lastname: Suh
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
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006624:Hippocampus / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008953:Models, Anatomic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique