title: A fast atlas pre-selection procedure for multi-atlas based brain segmentation.
abstract: Multi-atlas based MR image segmentation has been recognized as a quantitative analysis approach for brain. For such purpose, atlas databases keep increasing to include various anatomical characteristics of human brain. Atlas pre-selection becomes a necessary step for efficient and accurate automated segmentation of human brain images. In this study, we proposed a method of atlas pre-selection for target image segmentation on the MriCloud platform, which is a state-of-the-art multi-atlas based segmentation tool. In the MRIcloud pipeline, segmentation of lateral ventricle (LV) label is generated as an additional input in the segmentation pipeline. Under this circumstance, similarity of the LV label between target image and atlases was adopted as the atlas ranking scheme. Dice overlap coefficient was calculated and taken as the quantitative measure for atlas ranking. Segmentation results based on the proposed method were compared with that based on atlas pre-selection by mutual information (MI) between images. The final segmentation results showed a comparable accuracy of the proposed method with that from MI based atlas pre-selection. However, the computation load for the atlas pre-selection was speeded up by about 20 times compared to MI based pre-selection. The proposed method provides a promising assistance for quantitative analysis of brain images.
authors.forename: Jingbo
authors.initials: J
authors.lastname: Ma
authors.forename: Heather T
authors.initials: HT
authors.lastname: Ma
authors.forename: Hengtong
authors.initials: H
authors.lastname: Li
authors.forename: Chenfei
authors.initials: C
authors.lastname: Ye
authors.forename: Dan
authors.initials: D
authors.lastname: Wu
authors.forename: Xiaoying
authors.initials: X
authors.lastname: Tang
authors.forename: Michael
authors.initials: M
authors.lastname: Miller
authors.forename: Susumu
authors.initials: S
authors.lastname: Mori
mesh_terms: D000328:Adult; D000368:Aged; D001921:Brain / Q000473:pathology*; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008875:Middle Aged; D015233:Models, Statistical; D059906:Neuroimaging; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D055815:Young Adult