Joint Tumor Segmentation and Dense Deformable Registration of Brain MR Images

Abstract : In this paper we propose a novel graph-based concurrent registration and segmentation framework. Registration is modeled with a pairwise graphical model formulation that is modular with respect to the data and regularization term. Segmentation is addressed by adopt- ing a similar graphical model, using image-based classi cation techniques while producing a smooth solution. The two problems are coupled via a relaxation of the registration criterion in the presence of tumors as well as a segmentation through a registration term aiming the separation be- tween healthy and diseased tissues. E cient linear programming is used to solve both problems simultaneously. State of the art results demon- strate the potential of our method on a large and challenging low-grade glioma data set.
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Communication dans un congrès
MICCAI - 15th International Conference on Medical Image Computing and Computer-Assisted Intervention - 2012, Oct 2012, Nice, France. Springer Berlin Heidelberg, 7511, pp.651-658, 2012, Lecture Notes in Computer Science. 〈10.1007/978-3-642-33418-4_80〉
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Sarah Parisot, Hugues Duffau, Stéphane Chemouny, Nikos Paragios. Joint Tumor Segmentation and Dense Deformable Registration of Brain MR Images. MICCAI - 15th International Conference on Medical Image Computing and Computer-Assisted Intervention - 2012, Oct 2012, Nice, France. Springer Berlin Heidelberg, 7511, pp.651-658, 2012, Lecture Notes in Computer Science. 〈10.1007/978-3-642-33418-4_80〉. 〈hal-00773618〉

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