An Example-Based Multi-Atlas Approach to Automatic Labeling of White Matter Tracts

• Published in: PLOS ONE Vol. 10; no. 7; p. e0133337
• Main Authors: Yoo, Sang Wook, Guevara, Pamela, Jeong, Yong, Yoo, Kwangsun, Shin, Joseph S., Mangin, Jean-Francois, Seong, Joon-Kyung
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science (PLoS) 30.07.2015; Public Library of Science
• Subjects: Adult; Algorithms; Biomedical engineering; Brain; Bundles; Bundling; Classification; Computer applications; Computer Graphics; Computer science; Computing time; Dietary fiber; Diffusion Tensor Imaging; Diffusion Tensor Imaging - methods; Diffusion Tensor Imaging - statistics & numerical data; Engineering; Exploitation; Graphics processing units; Hemispheres; Humans; Imaging, Three-Dimensional; Imaging, Three-Dimensional - methods; Imaging, Three-Dimensional - statistics & numerical data; Labeling; Labelling; Male; Medicine; Methods; Models, Anatomic; Models, Neurological; Multivariate analysis; Neural Pathways; Neural Pathways - anatomy & histology; Neuroimaging; Neuroimaging - methods; Neuroimaging - statistics & numerical data; Q; R; Registration; Research Article; Science; Scleroderma; Studies; Substantia alba; White Matter; White Matter - anatomy & histology; Young Adult
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0133337

We present an example-based multi-atlas approach for classifying white matter (WM) tracts into anatomic bundles. Our approach exploits expert-provided example data to automatically classify the WM tracts of a subject. Multiple atlases are constructed to model the example data from multiple subjects in order to reflect the individual variability of bundle shapes and trajectories over subjects. For each example subject, an atlas is maintained to allow the example data of a subject to be added or deleted flexibly. A voting scheme is proposed to facilitate the multi-atlas exploitation of example data. For conceptual simplicity, we adopt the same metrics in both example data construction and WM tract labeling. Due to the huge number of WM tracts in a subject, it is time-consuming to label each WM tract individually. Thus, the WM tracts are grouped according to their shape similarity, and WM tracts within each group are labeled simultaneously. To further enhance the computational efficiency, we implemented our approach on the graphics processing unit (GPU). Through nested cross-validation we demonstrated that our approach yielded high classification performance. The average sensitivities for bundles in the left and right hemispheres were 89.5% and 91.0%, respectively, and their average false discovery rates were 14.9% and 14.2%, respectively.