title: Automated cerebellar lobule segmentation with application to cerebellar structural analysis in cerebellar disease.
abstract: The 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 α-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.
authors.affiliation: Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA. Electronic address: zyang11@jhu.edu.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Yang
authors.affiliation: Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation, The Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Chuyang
authors.initials: C
authors.lastname: Ye
authors.affiliation: Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
authors.forename: John A
authors.initials: JA
authors.lastname: Bogovic
authors.affiliation: Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA.
authors.forename: Aaron
authors.initials: A
authors.lastname: Carass
authors.affiliation: Department of Applied Math and Statistics, The Johns Hopkins University, Baltimore, MD 21218, USA.
authors.forename: Bruno M
authors.initials: BM
authors.lastname: Jedynak
authors.affiliation: Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA.
authors.forename: Sarah H
authors.initials: SH
authors.lastname: Ying
authors.affiliation: Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Applied Math and Statistics, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA.
authors.forename: Jerry L
authors.initials: JL
authors.lastname: Prince
mesh_terms: D002531:Cerebellum / Q000473:pathology*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods; D020754:Spinocerebellar Ataxias / Q000473:pathology*
keywords: Cerebellar lobule segmentation; Cerebellum; Graph cuts; Magnetic resonance imaging; Multi-atlas labeling; Random forest classifier; Spinocerebellar ataxia