Automatic Brain MRI Segmentation in Very Preterm Infants

Lili He¹, Nehal A. Parikh¹

Premature birth and secondary perinatal-neonatal insults profoundly alter brain development and functional outcomes. Accurate gray and white matter tissue quantification of neonatal brain MRI scans can enhance our understanding of this developmental trajectory and serve as diagnostic and prognostic biomarkers. However, accurate quantification remains an extremely challenging task in very pdue to a combination of factors, including lower image contrast (due to incomplete myelination at this isointense stage), lower signal-to-noise ratio (shorter scan times), greater motion artifacts, and lower spatial resolution (smaller head size) as compared to adult brain scans. In this paper, we present a fully automated and computational efficient spatial fuzzy segmentation algorithm, which we first proposed to detect activation regions in functional MRI. The method is validated both qualitatively and quantitatively on simulated and in-vivo extremely low birth weight (ELBW; BW 1000g) infant brain MRI scans at term-equivalent age.