2005 | OriginalPaper | Buchkapitel
Semi-automated Basal Ganglia Segmentation Using Large Deformation Diffeomorphic Metric Mapping
verfasst von : Ali Khan, Elizabeth Aylward, Patrick Barta, Michael Miller, M. Faisal Beg
Erschienen in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005
Verlag: Springer Berlin Heidelberg
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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.