Sub-acute and chronic ischemic stroke lesion MRI segmentation

Abstract

Automatic segmentation of chronic stroke lesion from magnetic resonance images (MRI) is motivated by the increasing need for reproducible and repeatable endpoints in clinical trials. The task is non-trivial, due to a number of confounding factors, including heterogeneous lesion intensity, irregular shape, and large deformations that render the conventional use of prior probabilistic atlases challenging. In this paper, we introduce a hidden Markov random field model that avails of a novel prior probabilistic vascular territory atlas to describe the natural vascular constraints in the brain. The vascular territory atlas is deformed in a joint registration-segmentation framework to overcome subject-specific morphological variability. T1-w and Flair sequences are used to populate our model, and a variational approach is implemented to find a solution. The performance of our model is demonstrated on two datasets, and compared to manual delineations by expert raters.