Advances in neonatal care have improved the survival of infants born prematurely although these infants remain at increased risk of adverse neurodevelopmental outcome. The measurement of white matter structure and features of the cortical surface can help define biomarkers that predict this risk. The measurement of these structures relies upon accurate automated segmentation routines, but these are often confounded by neonatal-specific imaging difficulties including poor contrast, low resolution, partial volume effects and the presence of significant natural and pathological anatomical variability. In this work we develop and evaluate an adaptive preterm multi-modal maximum a posteriori expectation-maximisation segmentation algorithm (AdaPT) incorporating an iterative relaxation strategy that adapts the tissue proportion priors toward the subject data. Also incorporated are intensity non-uniformity correction, a spatial homogeneity term in the form of a Markov random field and furthermore, the proposed method explicitly models the partial volume effect specifically mitigating the neonatal specific grey and white matter contrast inversion. Spatial priors are iteratively relaxed, enabling the segmentation of images with high anatomical disparity from a normal population. Experiments performed on a clinical cohort of 92 infants are validated against manual segmentation of normal and pathological cortical grey matter, cerebellum and ventricular volumes. Dice overlap scores increase significantly when compared to a widely-used maximum likelihood expectation maximisation algorithm for pathological cortical grey matter, cerebellum and ventricular volumes. Adaptive maximum a posteriori expectation maximisation is shown to be a useful tool for accurate and robust neonatal brain segmentation.
Copyright © 2012 Elsevier Inc. All rights reserved.