Concurrent tumor segmentation and registration with uncertainty-based sparse non-uniform graphs.
In this paper, we present a graph-based concurrent brain tumor segmentation and atlas to diseased patient registration framework. Both segmentation and registration problems are modeled using a unified pairwise discrete Markov Random Field model on a sparse grid superimposed to the image domain. Segmentation is addressed based on pattern classification techniques, while registration is performed by maximizing the similarity between volumes and is modular with respect to the matching criterion. The two problems are coupled by relaxing the registration term in the tumor area, corresponding to areas of high classification score and high dissimilarity between volumes. In order to overcome the main shortcomings of discrete approaches regarding appropriate sampling of the solution space as well as important memory requirements, content driven samplings of the discrete displacement set and the sparse grid are considered, based on the local segmentation and registration uncertainties recovered by the min marginal energies. State of the art results on a substantial low-grade glioma database demonstrate the potential of our method, while our proposed approach shows maintained performance and strongly reduced complexity of the model.
Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK. Electronic address: s.parisot@imperial.ac.uk.
Sarah
S
Parisot
Surgical Planning Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, MA, USA.
William
W
Wells
Intrasense SAS, Montpellier, France.
Stéphane
S
Chemouny
Department of Neurosurgery, Hopital Gui de Chauliac, Montpellier, France.
Hugues
H
Duffau
Center for Visual Computing, Ecole Centrale Paris, Châtenay Malabry, France; Equipe GALEN, INRIA Saclay - Ile de France, Orsay, France.
Nikos
N
Paragios
D001921:Brain / Q000473:pathology*; D001932:Brain Neoplasms / Q000473:pathology*; D005910:Glioma / Q000473:pathology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D010363:Pattern Recognition, Automated / Q000379:methods
Brain tumors; Concurrent segmentation/registration; Markov Random Fields; Min-marginals