1. Sub-acute and Chronic Ischemic Stroke Lesion MRI Segmentation
- Author
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Olivier Detante, Senan Doyle, Olivier Heck, Assia Jaillard, Florence Forbes, and Michel Dojat
- Subjects
medicine.diagnostic_test ,Computer science ,Atlas (topology) ,business.industry ,05 social sciences ,Probabilistic logic ,Pattern recognition ,Magnetic resonance imaging ,Sub acute ,Fluid-attenuated inversion recovery ,050105 experimental psychology ,Lesion ,03 medical and health sciences ,0302 clinical medicine ,Ischemic stroke ,medicine ,0501 psychology and cognitive sciences ,Artificial intelligence ,medicine.symptom ,Hidden Markov random field ,business ,030217 neurology & neurosurgery - 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.
- Published
- 2018