Local Label Learning (LLL) for Subcortical Structure Segmentation: Application to Hippocampus Segmentation
文献类型：期刊论文
| 作者 | Hao, Yongfu1; Wang, Tianyao2; Zhang, Xinqing3; Duan, Yunyun4; Yu, Chunshui5; Jiang, Tianzi1 ; Fan, Yong1; Alzheimer's Dis Neuroimaging
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| 刊名 | HUMAN BRAIN MAPPING
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| 出版日期 | 2014-06-01 |
| 卷号 | 35期号:6页码:2674-2697 |
| 关键词 | multi-atlas based segmentation local label learning hippocampal segmentation SVM |
| 英文摘要 | Automatic and reliable segmentation of subcortical structures is an important but difficult task in quantitative brain image analysis. Multi-atlas based segmentation methods have attracted great interest due to their promising performance. Under the multi-atlas based segmentation framework, using deformation fields generated for registering atlas images onto a target image to be segmented, labels of the atlases are first propagated to the target image space and then fused to get the target image segmentation based on a label fusion strategy. While many label fusion strategies have been developed, most of these methods adopt predefined weighting models that are not necessarily optimal. In this study, we propose a novel local label learning strategy to estimate the target image's segmentation label using statistical machine learning techniques. In particular, we use a L1-regularized support vector machine (SVM) with a k nearest neighbor (kNN) based training sample selection strategy to learn a classifier for each of the target image voxel from its neighboring voxels in the atlases based on both image intensity and texture features. Our method has produced segmentation results consistently better than state-of-the-art label fusion methods in validation experiments on hippocampal segmentation of over 100 MR images obtained from publicly available and in-house datasets. Volumetric analysis has also demonstrated the capability of our method in detecting hippocampal volume changes due to Alzheimer's disease. Hum Brain Mapp 35:2674-2697, 2014. (c) 2013 Wiley Periodicals, Inc. |
| WOS标题词 | Science & Technology ; Life Sciences & Biomedicine |
| 类目[WOS] | Neurosciences ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging |
| 研究领域[WOS] | Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging |
| 关键词[WOS] | ATLAS-BASED SEGMENTATION ; WHOLE-BRAIN SEGMENTATION ; IMAGE SEGMENTATION ; ALZHEIMERS-DISEASE ; AUTOMATIC SEGMENTATION ; LINEAR CLASSIFICATION ; SELECTION-STRATEGIES ; REGISTRATION ; MRI ; FUSION |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000334555100015 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3149]  |
| 专题 | 自动化研究所_脑网络组研究中心 |
| 作者单位 | 1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Brainnetome Ctr, Beijing 100190, Peoples R China 2.Shanghai East Hosp, Dept Radiol, Shanghai, Peoples R China 3.Capital Med Univ, Xuanwu Hosp, Dept Neurol, Beijing, Peoples R China 4.Capital Med Univ, Xuanwu Hosp, Dept Radiol, Beijing, Peoples R China 5.Tianjin Med Univ, Gen Hosp, Dept Radiol, Tianjin, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hao, Yongfu,Wang, Tianyao,Zhang, Xinqing,et al. Local Label Learning (LLL) for Subcortical Structure Segmentation: Application to Hippocampus Segmentation[J]. HUMAN BRAIN MAPPING,2014,35(6):2674-2697. |
| APA | Hao, Yongfu.,Wang, Tianyao.,Zhang, Xinqing.,Duan, Yunyun.,Yu, Chunshui.,...&Alzheimer's Dis Neuroimaging.(2014).Local Label Learning (LLL) for Subcortical Structure Segmentation: Application to Hippocampus Segmentation.HUMAN BRAIN MAPPING,35(6),2674-2697. |
| MLA | Hao, Yongfu,et al."Local Label Learning (LLL) for Subcortical Structure Segmentation: Application to Hippocampus Segmentation".HUMAN BRAIN MAPPING 35.6(2014):2674-2697. |
入库方式： OAI收割
来源：自动化研究所
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