Paper Info
Title
Automated Segmentation of Cerebellum Using Brain Mask and Partial Volume Estimation Map
Abstract
While segmentation of the cerebellum is an indispensable step in many studies, its contrast is not clear because of the adjacent cerebrospinal fluid, meninges, and cerebra peduncle. Thus, various cerebellar segmentation methods, such as a deformable model or a template-based algorithm might exhibit incorrect segmentation of the venous sinuses and the cerebellar peduncle. In this study, we propose a fully automated procedure combining cerebellar tissue classification, a template-based approach, and morphological operations sequentially. The cerebellar region was defined approximately by removing the cerebral region from the brain mask. Then, the noncerebellar region was trimmed using a morphological operator and the brain-stem atlas was aligned to the individual brain to define the brain-stem area. The proposed method was validated with the well-known FreeSurfer and ITK-SNAP packages using the dice similarity index and recall and precision scores. As a result, the proposed method was significantly better than the other methods for the dice similarity index (0.93, FreeSurfer: 0.92, ITK-SNAP: 0.87) and precision (0.95, FreeSurfer: 0.90, ITK-SNAP: 0.93). Therefore, it could be said that the proposed method yielded a robust and accurate segmentation result. Moreover, additional postprocessing with the brain-stem atlas could improve its result.
Year
DOI
Venue
2015
10.1155/2015/167489
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
Field
DocType
Volume
Brain mapping,Computer vision,Computer science,Segmentation,Precision and recall,Cerebellar peduncle,Peduncle (anatomy),Artificial intelligence,Partial volume,Cerebellum
Journal
2015
ISSN
Citations 
PageRank 
1748-670X
1
0.35
References 
Authors
11
4
Name
Order
Citations
PageRank
Dong-Kyun Lee110.35
Uicheul Yoon2987.61
Ki-Chang Kwak310.68
Jong-Min Lee419416.44