title: Adaptive neonate brain segmentation.
abstract: Babies born prematurely are at increased risk of adverse neurodevelopmental outcomes. Recent advances suggest that measurement of brain volumes can help in defining biomarkers for neurodevelopmental outcome. These techniques rely on an accurate segmentation of the MRI data. However, due to lack of contrast, partial volume (PV) effect, the existence of both hypo- and hyper-intensities and significant natural and pathological anatomical variability, the segmentation of neonatal brain MRI is challenging. We propose a pipeline for image segmentation that uses a novel multi-model Maximum a posteriori Expectation Maximisation (MAP-EM) segmentation algorithm with a prior over both intensities and the tissue proportions, a B0 inhomogeneity correction, and a spatial homogeneity term through the use of a Markov Random Field. This robust and adaptive technique enables the segmentation of images with high anatomical disparity from a normal population. Furthermore, the proposed method implicitly models Partial Volume, mitigating the problem of neonatal white/grey matter intensity inversion. Experiments performed on a clinical cohort show expected statistically significant correlations with gestational age at birth and birthweight. Furthermore, the proposed method obtains statistically significant improvements in Dice scores when compared to the a Maximum Likelihood EM algorithm.
authors: Centre for Medical Image Computing (CMIC), University College London, UK.
authors: M Jorge
authors: MJ
authors: Cardoso
authors: Andrew
authors: A
authors: Melbourne
authors: Giles S
authors: GS
authors: Kendall
authors: Marc
authors: M
authors: Modat
authors: Cornelia F
authors: CF
authors: Hagmann
authors: Nicola J
authors: NJ
authors: Robertson
authors: Neil
authors: N
authors: Marlow
authors: Sebastien
authors: S
authors: Ourselin
mesh_terms: D000465:Algorithms; D001921:Brain / Q000254:growth & development* / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D002555:Cerebrospinal Fluid; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D007231:Infant, Newborn; D007234:Infant, Premature; D016013:Likelihood Functions; D008390:Markov Chains; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results