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Segmentation of the Cerebellar Peduncles Using a Random Forest Classifier and a Multi-object Geometric Deformable Model: Application to Spinocerebellar Ataxia Type 6
Ye, Chuyang; Yang, Zhen; Ying, Sarah; Prince, Jerry
Source PublicationNeuroinformatics
2015-07
Volume13Issue:3Pages:367-381
AbstractThe cerebellar peduncles, comprising the superior cerebellar peduncles (SCPs), the middle cerebellar
peduncle (MCP), and the inferior cerebellar peduncles
(ICPs), are white matter tracts that connect the cerebellum
to other parts of the central nervous system. Methods for
automatic segmentation and quantification of the cerebellar
peduncles are needed for objectively and efficiently studying their structure and function. Diffusion tensor imaging
(DTI) provides key information to support this goal, but
it remains challenging because the tensors change dramatically in the decussation of the SCPs (dSCP), the region
where the SCPs cross. This paper presents an automatic
method for segmenting the cerebellar peduncles, including
the dSCP. The method uses volumetric segmentation concepts based on extracted DTI features. The dSCP and
noncrossing portions of the peduncles are modeled as separate objects, and are initially classified using a random forest
classifier together with the DTI features. To obtain geometrically correct results, a multi-object geometric deformable
model is used to refine the random forest classification.
The method was evaluated using a leave-one-out crossvalidation on five control subjects and four patients with
spinocerebellar ataxia type 6 (SCA6). It was then used to
evaluate group differences in the peduncles in a population
of 32 controls and 11 SCA6 patients. In the SCA6 group,
we have observed significant decreases in the volumes

of the dSCP and the ICPs and significant increases in the
mean diffusivity in the noncrossing SCPs, the MCP, and the
ICPs. These results are consistent with a degeneration of the
cerebellar peduncles in SCA6 patients.
KeywordCerebellar Peduncles Random Forest Classifier
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20340
Collection脑网络组研究中心
AffiliationJohns Hopkins University
Recommended Citation
GB/T 7714
Ye, Chuyang,Yang, Zhen,Ying, Sarah,et al. Segmentation of the Cerebellar Peduncles Using a Random Forest Classifier and a Multi-object Geometric Deformable Model: Application to Spinocerebellar Ataxia Type 6[J]. Neuroinformatics,2015,13(3):367-381.
APA Ye, Chuyang,Yang, Zhen,Ying, Sarah,&Prince, Jerry.(2015).Segmentation of the Cerebellar Peduncles Using a Random Forest Classifier and a Multi-object Geometric Deformable Model: Application to Spinocerebellar Ataxia Type 6.Neuroinformatics,13(3),367-381.
MLA Ye, Chuyang,et al."Segmentation of the Cerebellar Peduncles Using a Random Forest Classifier and a Multi-object Geometric Deformable Model: Application to Spinocerebellar Ataxia Type 6".Neuroinformatics 13.3(2015):367-381.
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