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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.
Cited literature [16 references]
https://hal.inria.fr/hal-00773618
Contributor : Sarah Parisot Connect in order to contact the contributor
Submitted on : Monday, January 14, 2013 - 1:56:22 PM
Last modification on : Tuesday, March 15, 2022 - 9:48:09 AM
Long-term archiving on: : Saturday, April 1, 2017 - 4:28:41 AM
Contributor : Sarah Parisot Connect in order to contact the contributor
Submitted on : Monday, January 14, 2013 - 1:56:22 PM
Last modification on : Tuesday, March 15, 2022 - 9:48:09 AM
Long-term archiving on: : Saturday, April 1, 2017 - 4:28:41 AM
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MICCAI12_Parisot.pdf
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