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 adopting a similar graphical model, using image-based classification 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 between healthy and diseased tissues. Efficient linear programming is used to solve both problems simultaneously. State of the art results demonstrate the potential of our method on a large and challenging low-grade glioma data set.

Diffuse WHO grade II gliomas are diffusively infiltrative brain tumors characterized by an unavoidable anaplastic transformation. Their management is strongly dependent on their location in the brain due to interactions with functional regions and potential differences in molecular biology. In this paper, we present the construction of a probabilistic atlas mapping the preferential locations of diffuse WHO grade II gliomas in the brain. This is carried out through a sparse graph whose nodes correspond to clusters of tumors clustered together based on their spatial proximity. The interest of such an atlas is illustrated via two applications. The first one correlates tumor location with the patient’s age via a statistical analysis, highlighting the interest of the atlas for studying the origins and behavior of the tumors. The second exploits the fact that the tumors have preferential locations for automatic segmentation. Through a coupled decomposed Markov Random Field model, the atlas guides the segmentation process, and characterizes which preferential location the tumor belongs to and consequently which behavior it could be associated to. Leave-one-out cross validation experiments on a large database highlight the robustness of the graph, and yield promising segmentation results.

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.

We investigate over a 12-year period the association between regional cerebral blood flow (CBF) and cardiovascular risk factors in a prospective cohort of healthy older adults (81.96 ± 3.82 year-old) from the Cognitive REServe and Clinical ENDOphenotype (CRESCENDO) study. Cardiovascular risk factors were measured over 12 years, and gray matter CBF was measured at the end of the study from high-resolution magnetic resonance imaging using arterial spin labeling. The association between cardiovascular risk factors, their long-term change, and CBF was assessed using multivariate linear regression models. Women were observed to have higher CBF than men (p < 0.05). Increased mean arterial pressure (MAP) over the 12-year period was correlated with a low cerebral blood flow (p < 0.05, R(2) = 0.21), whereas no association was detected between CBF and MAP at the time of imaging. High levels of glycemia tended to be associated with low cerebral blood flow values (p < 0.05). Age, alcohol consumption, smoking status, body mass index, history of cardiovascular disease, and hypertension were not associated with CBF. Our main result suggests that change in MAP is the most significant predictor of future CBF in older adults.

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