Semi-automated basal ganglia segmentation using large deformation diffeomorphic metric mapping.
This paper investigates the techniques required to produce accurate and reliable segmentations via grayscale image matching. Finding a large deformation, dense, non-rigid transformation from a template image to a target image allows us to map a template segmentation to the target image space, and therefore compute the target image segmentation and labeling. We outline a semi-automated procedure involving landmark and image intensity-based matching via the large deformation diffeomorphic mapping metric (LDDMM) algorithm. Our method is applied specifically to the segmentation of the caudate nucleus in pre- and post-symptomatic Huntington's Disease (HD) patients. Our accuracy is compared against gold-standard manual segmentations and various automated segmentation tools through the use of several error metrics.
Khan
Ali
A
School of Engineering Science, Simon Fraser University, 8888 University Drive, Burnaby BC, V5A 1S6, Canada.
Aylward
Elizabeth
E
Barta
Patrick
P
Miller
Michael
M
Beg
M Faisal
MF