Assessing early brain development in neonates by segmentation of high-resolution 3T MRI

Abstract

This paper describes effort towards automatic tissue segmentation in neonatal MRI. Extremely low contrast to noise ratio (CNR), regional intensity changes due to RF coil inhomogeneity and biology, and tissue property changes due to the early myelination and axon pruning processes require a methodology that combines the strength of spatial priors (template atlas), data modelling, and prior knowledge about brain development. We use an EM-type algorithm that includes tissue classification, inhomogeneity correction and brain stripping into an iterative optimization scheme using a mixture distribution model. A statistical brain atlas registered to the subject image serves as a spatial prior. White matter in neonates is modeled as a mixture model of non-myelinated and myelinated regions. A pilot study on 10 neonates demonstrates the feasibility of high-resolution neonatal MRI and of automatic tissue segmentation. Results demonstrate that interleaved segmentation and inhomogeneity correction, guided by a statistical spatial prior, will provide a powerful and efficient segmentation framework for this type of imaging data. It is demonstrated that the mixture model for white matter allows us to segment early myelination regions of the projection tract up to the motor cortex, while also providing non-myelinated white, gray and csf segmentation. The early myelination regions are hypothesized to develop early but have not, yet been shown in quantitative MRI studies.