Computational analysis: A bridge to translational stroke treatment

Abstract

Objective rapid quantification of injury using computational methods can improve the assessment of the degree of stroke injury, aid in the selection of patients for early or specific treatments, and monitor the evolution of injury and recovery. In this chapter, we use neonatal ischemia as a case-study of the application of several computational methods that in fact are generic and applicable across the age and disease spectrum. We provide a summary of current computational approaches used for injury detection, including Gaussian mixture models (GMM), Markov random fields (MRFs), normalized graph cut, and K-means clustering. We also describe more recent automated approaches to segment the region(s) of ischemic injury including hierarchical region splitting, support vector machine, a brain symmetry/asymmetry integrated model, and a watershed method that are robust at different developmental stages. We conclude with our assessment of probable future research directions in the field of computational noninvasive stroke analysis such as automated detection of the ischemic core and penumbra, monitoring