SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Model-Based Segmentation of Hippocampal Subfields in Ultra-High Resolution In Vivo MRI
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Supervised Nonparametric Image Parcellation
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Asymmetric Image-Template Registration
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Task-Optimal Registration Cost Functions
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Prior knowledge driven multiscale segmentation of brain MRI
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
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
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This paper addresses the problem of creating probabilistic brain atlases from manually labeled training data. We propose a general mesh-based atlas representation, and compare different atlas models by evaluating their posterior probabilities and the posterior probabilities of their parameters. Using such a Baysian framework, we show that the widely used ”average” brain atlases constitute relatively poor priors, partly because they tend to overfit the training data, and partly because they do not allow to align corresponding anatomical features across datasets. We also demonstrate that much more powerful representations can be built using content-adaptive meshes that incorporate non-rigid deformation field models. We believe extracting optimal prior probability distributions from training data is crucial in light of the central role priors play in many automated brain MRI analysis techniques.