Automatic segmentation of neonatal brain MRI using atlas based segmentation and machine learning approach

Volumetric measurements of neonatal brain tissue classes have been suggested as an indicator of long-term neurodevelopmental performance. To obtain these measurements, accurate brain tissue segmentation is needed. We propose a novel method for segmentation of axial neonatal brain MRI that combines multi-atlas-based segmentation and supervised voxel classification to segment eight different tissue classes, namely cortical grey matter (CoGM), unmyelinated white matter (UWM), brainstem, cerebellum, ventricles, cerebrospinal fluid in the extracerebral space (CSF), basal ganglia and thalami, myelinated white matter (MWM). The evaluation was performed in the framework of MICCAI NeoBrainS12 challenge. The obtained results indicate that proposed approach generated accurate segmentation results for all tissues, except for MWM.