Liat Ein-Dor, Y. Goldschmidt, et al.
IBM J. Res. Dev
A supervised framework is presented for the automatic registration and segmentation of white matter (WM) tractographies extracted from brain DT-MRI. The framework relies on the direct registration between the fibers, without requiring any intensity-based registration as preprocessing. An affine transform is recovered together with a set of segmented fibers. A recently introduced probabilistic boosting tree classifier is used in a segmentation refinement step to improve the precision of the target tract segmentation. The proposed method compares favorably with a state-of-the-art intensity-based algorithm for affine registration of DTI tractographies. Segmentation results for 12 major WM tracts are demonstrated. Quantitative results are also provided for the segmentation of a particularly difficult case, the optic radiation tract. An average precision of 80% and recall of 55% were obtained for the optimal configuration of the presented method. © 2010 IEEE.
Liat Ein-Dor, Y. Goldschmidt, et al.
IBM J. Res. Dev
Eric Price, David P. Woodruff
FOCS 2011
A. Gupta, R. Gross, et al.
SPIE Advances in Semiconductors and Superconductors 1990
Yao Qi, Raja Das, et al.
ISSTA 2009