Short association bundle atlas based on inter-subject clustering from HARDI data.
Annu Int Conf IEEE Eng Med Biol Soc
; 2016: 5545-5549, 2016 Aug.
Article
in En
| MEDLINE
| ID: mdl-28269513
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Image Processing, Computer-Assisted
/
Brain
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Annu Int Conf IEEE Eng Med Biol Soc
Year:
2016
Document type:
Article
Country of publication:
United States