MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection
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- 作者：Jimit Doshia ; 1 ; Guray Erusa ; 1 ; jimit.doshi@uphs.upenn.edu" class="auth_mail" title="E-mail the corresponding author ; Yangming Oua ; b ; Susan M. Resnickc ; Ruben C. Gurd ; Raquel E. Gurd ; Theodore D. Satterthwaited ; Susan Furthe ; Christos Davatzikosa ; for the Alzheimer's Neuroimaging Initiative2
- 关键词：MRI ; Multi-atlas segmentation ; Registration ; Label fusion ; ROI
- 刊名：NeuroImage
- 出版年：2016
- 出版时间：15 February 2016
- 年：2016
- 卷：127
- 期：Complete
- 页码：186-195
- 全文大小：1286 K
文摘
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A new multiatlas segmentation framework using a broad ensemble of labeled templates
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Combines different atlases, warping algorithms, and regularization parameters
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Uses an adaptive fusion strategy through local similarity weighting and intensity based refinement
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Ensemble approach provides robustness to image variations and produces accurate segmentations.