@inproceedings{4929db18a83143c38f9a7df98669353a,
title = "Semi-automated basal ganglia segmentation using large deformation diffeomorphic metric mapping",
abstract = "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.",
author = "Ali Khan and Elizabeth Aylward and Patrick Barta and Michael Miller and Beg, {M. Faisal}",
year = "2005",
doi = "10.1007/11566465_30",
language = "English (US)",
isbn = "3540293272",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "238--245",
booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 - 8th International Conference, Proceedings",
note = "8th International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 ; Conference date: 26-10-2005 Through 29-10-2005",
}