Uncertainty-driven Efficiently-Sampled Sparse Graphical Models for Concurrent Tumor Segmentation and Atlas Registration

Sarah Parisot (1, 2, 3) , William Wells Iii (4) , Stéphane Chemouny (3) , Hugues Duffau (5) , Nikos Paragios (1, 2)

1 CVN - Centre de vision numérique
2 GALEN - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content
3 Intrasense SAS
4 Surgical Planning Lab [Boston]
5 Hôpital Gui de Chauliac [CHU Montpellier]

Graph-based methods have become popular in recent years and have successfully addressed tasks like segmentation and deformable registration. Their main strength is optimality of the obtained solution while their main limitation is the lack of precision due to the grid-like representations and the discrete nature of the quantized search space. In this paper we introduce a novel approach for combined segmentation/registration of brain tumors that adapts graph and sampling resolution according to the image content. To this end we estimate the segmentation and registration marginals towards adaptive graph resolution and intelligent definition of the search space. This information is considered in a hierarchical framework where uncertainties are propagated in a natural manner. State of the art results in the joint segmentation/registration of brain images with low-grade gliomas demonstrate the potential of our approach.

Domaines: Imagerie médicale

Sarah Parisot, William Wells Iii, Stéphane Chemouny, Hugues Duffau, Nikos Paragios. Uncertainty-driven Efficiently-Sampled Sparse Graphical Models for Concurrent Tumor Segmentation and Atlas Registration. ICCV - 14th International Conference on Computer Vision, Dec 2013, Sydney, Australia. ⟨hal-00858696⟩