Active shape models—their training and application
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Unified Framework for Atlas Matching Using Active Appearance Models
IPMI '99 Proceedings of the 16th International Conference on Information Processing in Medical Imaging
Automatic 3D ASM Construction via Atlas-Based Landmarking and Volumetric Elastic Registration
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Deformable M-Reps for 3D Medical Image Segmentation
International Journal of Computer Vision - Special Issue on Research at the University of North Carolina Medical Image Display Analysis Group (MIDAG)
Comparison and Evaluation of Segmentation Techniques for Subcortical Structures in Brain MRI
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Classifier selection strategies for label fusion using large atlas databases
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Effects of registration regularization and atlas sharpness on segmentation accuracy
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Deformable object modelling and matching
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
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We describe an efficient and accurate method for segmenting sets of subcortical structures in 3D MR images of the brain. We first find the approximate position of all the structures using a global Active Appearance Model (AAM). We then refine the shape and position of each structure using a set of individual AAMs trained for each. Finally we produce a detailed segmentation by computing the probability that each voxel belongs to the structure, using regression functions trained for each individual voxel. The models are trained using a large set of labelled images, using a novel variant of `groupwise' registration to obtain the necessary image correspondences. We evaluate the method on a large dataset, and demonstrate that it achieves results comparable with some of the best published.