Short association bundle atlas based on inter-subject clustering from HARDI data

• Published in: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2016; pp. 5545 - 5549
• Main Authors: Roman, C., Guevara, M., Duclap, D., Lebois, A., Poupon, C., Mangin, J.-F, Guevara, P.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.08.2016
• Subjects: Algorithm design and analysis; Algorithms; Brain - anatomy & histology; Brain - diagnostic imaging; Cluster Analysis; Connectome; Databases, Factual; Diffusion Magnetic Resonance Imaging - methods; Diffusion tensor imaging; Humans; Image Processing, Computer-Assisted - methods; Labeling; Manuals; Protocols; Reproducibility of Results; White Matter - diagnostic imaging
• ISSN: 1557-170X
• DOI: 10.1109/EMBC.2016.7591983

This paper is focused on the study of short brain association fibers. We present an automatic method to identify short bundles of the superficial white matter based on inter-subject hierarchical clustering. Our method finds clusters of similar fibers, belonging to the different subjects, according to a distance measure between fibers. First, the algorithm obtains representative bundles and subsequently we perform an automatic labeling based on the anatomy, of the most stable connections. The analysis was applied to two independent groups of 37 subjects. Results between the two groups were compared, in order to keep reproducible connections for the atlas creation. The method was applied using linear and non-linear registration, where the non-linear registration showed significantly better results. A final atlas with 35 bundles in the left hemisphere and 27 in the right hemisphere from the whole brain was obtained. Finally results were validated using the atlas to segment 26 new subjects from another HARDI database.