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Proceedings Paper

Local label learning (L3) for multi-atlas based segmentation
Author(s): Yongfu Hao; Jieqiong Liu; Yunyun Duan; Xinqing Zhang; Chunshui Yu; Tianzi Jiang; Yong Fan
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Paper Abstract

For subcortical structure segmentation, multi-atlas based segmentation methods have attracted great interest due to their competitive performance. Under this framework, using deformation fields generated for registering atlas images to the target image, labels of the atlases are first propagated to the target image space and further fused somehow to get the target segmentation. Many label fusion strategies have been proposed and most of them adopt predefined weighting models which are not necessarily optimal. In this paper, we propose a local label learning (L3) strategy to estimate the target image's label using statistical machine learning techniques. Specifically, we use Support Vector Machine (SVM) to learn a classifier for each of the target image voxels using its neighboring voxels in the atlases as a training dataset. Each training sample has dozens of image features extracted around its neighborhood and these features are optimally combined by the SVM learning method to classify the target voxel. The key contribution of this method is the development of a locally specific classifier for each target voxel based on informative texture features. The validation experiment on 57 MR images has demonstrated that our method generates segmentation results of hippocampal with a dice overlap of 0.908±0.023 to manual segmentations, statistically significantly better than state-of-the-art segmentation algorithms.

Paper Details

Date Published: 14 February 2012
PDF: 8 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83142E (14 February 2012); doi: 10.1117/12.911014
Show Author Affiliations
Yongfu Hao, Institute of Automation (China)
Jieqiong Liu, Xuanwu Hospital of Capital Medical Univ. (China)
Yunyun Duan, Xuanwu Hospital of Capital Medical Univ. (China)
Xinqing Zhang, Xuanwu Hospital of Capital Medical Univ. (China)
Chunshui Yu, Tianjin Medical Univ. General Hospital (China)
Tianzi Jiang, Institute of Automation (China)
Yong Fan, Institute of Automation (China)


Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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