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Automated cerebellar lobule segmentation with application to cerebellar structural analysis in cerebellar disease
Yang, Zhen1; Ye, Chuyang2,3; Bogovic, John A.4; Carass, Aaron1,5; Jedynak, Bruno M.6; Ying, Sarah H.7; Prince, Jerry L.1,5,6,7; Zhen Yang
Source PublicationNEUROIMAGE
2016-02-15
Volume127Issue:1Pages:435-444
SubtypeArticle
AbstractThe cerebellum plays an important role in both motor control and cognitive function. Cerebellar function is topographically organized and diseases that affect specific parts of the cerebellum are associated with specific patterns of symptoms. Accordingly, delineation and quantification of cerebellar sub-regions from magnetic resonance images are important in the study of cerebellar atrophy and associated functional losses. This paper describes an automated cerebellar lobule segmentation method based on a graph cut segmentation framework. Results from multi-atlas labeling and tissue classification contribute to the region terms in the graph cut energy function and boundary classification contributes to the boundary term in the energy function. A cerebellar parcellation is achieved by minimizing the energy function using the a-expansion technique. The proposed method was evaluated using a leave-one-out cross-validation on 15 subjects including both healthy controls and patients with cerebellar diseases. Based on reported Dice coefficients, the proposed method outperforms two state-of-the-art methods. The proposed method was then applied to 77 subjects to study the region-specific cerebellar structural differences in three spinocerebellar ataxia (SCA) genetic subtypes. Quantitative analysis of the lobule volumes shows distinct patterns of volume changes associated with different SCA subtypes consistent with known patterns of atrophy in these genetic subtypes. (C) 2015 Elsevier Inc. All rights reserved.
KeywordCerebellum Cerebellar Lobule Segmentation Graph Cuts Magnetic Resonance Imaging Multi-atlas Labeling Random Forest Classifier Spinocerebellar Ataxia
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
DOI10.1016/j.neuroimage.2015.09.032
WOS KeywordIMAGE SEGMENTATION ; HUNTINGTONS-DISEASE ; STATISTICAL FUSION ; LABEL-FUSION ; GRAPH CUTS ; BRAIN ; ATLAS ; ATROPHY ; PERFORMANCE ; DEGENERATION
Indexed BySCI
Language英语
Funding OrganizationNIH/NINDS(5R01NS056307-08)
WOS Research AreaNeurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectNeurosciences ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000369952900037
Citation statistics
Cited Times:10[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11341
Collection脑网络组研究中心
Corresponding AuthorZhen Yang
Affiliation1.Johns Hopkins Univ, Dept Elect & Comp Engn, 105 Barton Hall,3400 N Charles St, Baltimore, MD 21218 USA
2.Chinese Acad Sci, Brainnetome Ctr, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
4.Howard Hughes Med Inst, Janelia Res Campus, Ashburn, VA 20147 USA
5.Johns Hopkins Univ, Dept Comp Sci, Baltimore, MD 21218 USA
6.Johns Hopkins Univ, Dept Appl Math & Stat, Baltimore, MD 21218 USA
7.Johns Hopkins Sch Med, Dept Radiol, Baltimore, MD 21287 USA
Recommended Citation
GB/T 7714
Yang, Zhen,Ye, Chuyang,Bogovic, John A.,et al. Automated cerebellar lobule segmentation with application to cerebellar structural analysis in cerebellar disease[J]. NEUROIMAGE,2016,127(1):435-444.
APA Yang, Zhen.,Ye, Chuyang.,Bogovic, John A..,Carass, Aaron.,Jedynak, Bruno M..,...&Zhen Yang.(2016).Automated cerebellar lobule segmentation with application to cerebellar structural analysis in cerebellar disease.NEUROIMAGE,127(1),435-444.
MLA Yang, Zhen,et al."Automated cerebellar lobule segmentation with application to cerebellar structural analysis in cerebellar disease".NEUROIMAGE 127.1(2016):435-444.
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