Average brain models: a convergence study
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
A Statistical Shape Model for the Liver
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
Automatic Construction of 3D Statistical Deformation Models Using Non-rigid Registration
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Non-parametric diffeomorphic image registration with the demons algorithm
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Least biased target selection in probabilistic atlas construction
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
3D shape analysis for liver-gallbladder anatomical structure retrieval
MICCAI'12 Proceedings of the 4th international conference on Abdominal Imaging: computational and clinical applications
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The construction of probabilistic liver atlases has received little attention in the past. Existing methods are based on landmarks and are sensitive to their choices and placements. We propose an iterative landmark-free method based on dense volumes to construct linear unbiased diffeomorphic probabilistic atlases from liver CT images. The linear averaging of the transformed images is set as the common target space followed by pairwise diffeomorphic registrations to warp all images to the target using a recent-proposed efficient deformation approach during each iteration cycle. Iterative pairwise registrations are directly used to handle possible large deformations without the need for an extra step to remove global deformations such as the use of affine transformations in traditional methods. Compared with those approaches estimating the unbiased atlas and the transformations groupwise simultaneously, the current method is more efficient. The efficiency and the convergence of our method have been demonstrated experimentally by validation using 25 CT liver sets.