Concurrent tumor segmentation and registration with uncertainty-based sparse non-uniform graphs

Abstract : 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.
Document type :
Journal articles
Liste complète des métadonnées

Cited literature [58 references]  Display  Hide  Download

https://hal.inria.fr/hal-01109692
Contributor : Sarah Parisot <>
Submitted on : Monday, January 26, 2015 - 5:17:29 PM
Last modification on : Thursday, February 7, 2019 - 5:29:11 PM
Document(s) archivé(s) le : Monday, April 27, 2015 - 10:56:43 AM

File

MedIA-V2.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Sarah Parisot, William Wells, Stéphane Chemouny, Hugues Duffau, Nikos Paragios. Concurrent tumor segmentation and registration with uncertainty-based sparse non-uniform graphs. Medical Image Analysis, Elsevier, 2014, 18 (4), pp.647 - 659. ⟨10.1016/j.media.2014.02.006⟩. ⟨hal-01109692⟩

Share

Metrics

Record views

197

Files downloads

198