International Journal of Computer Vision
Convergence Theorems for Generalized Alternating Minimization Procedures
The Journal of Machine Learning Research
LOCUS: local cooperative unified segmentation of MRI brain scans
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Fast and Robust 3-D MRI Brain Structure Segmentation
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
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In most approaches, tissue and subcortical structure segmentations of MR brain scans are handled globally over the entire brain volume through two relatively independent sequential steps. We propose a fully Bayesian joint model that integrates local tissue and structure segmentations and local intensity distributions. It is based on the specification of three conditional Markov Random Field (MRF) models. The first two encode cooperations between tissue and structure segmentations and integrate a priorianatomical knowledge. The third model specifies a Markovian spatial prior over the model parameters that enables local estimations while ensuring their consistency, handling this way nonuniformity of intensity without any bias field modelization. The complete joint model provides a sound theoretical framework for carrying out tissue and structure segmentation by distributing a set of local and cooperative MRF models. The evaluation, using a previously affine-registred atlas of 17 structures and performed on both phantoms and real 3T brain scans, shows good results.