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 automatic segmentation of cortical grey matter (CoGM), unmyelinated white
matter (UWM), myelinated white matter (MWM), basal ganglia and thalami, brainstem, cerebellum, ventricles,
and cerebrospinal fluid in the extracerebral space (CSF) in MRI scans of the brain in preterm infants.
For this project, seven preterm born infants, scanned at term equivalent age were used. Axial T1- and T2-
weighted scans were acquired with 3T MRI scanner. The automatic segmentation was performed in three
subsequent stages where each tissue was labeled. First, a multi-atlas-based segmentation (MAS) was employed
to obtain localized, subject specific spatially varying priors for each tissue. Next, based on these priors, two-class
classification with k-nearest neighbor (kNN) classifier was performed to obtain the segmentation of each tissue
type separately. Last, to refine the final result, and to achieve the segmentation along the tissue boundaries, a
multiclass naive Bayes classifier was employed. The results were evaluated against the manually set reference
standard and quantified in terms of Dice coefficient (DC) and modified Hausdorff distance (MHD), defined as
95th-percentile of the Hausdorff distance.
On average, the method achieved the following DCs: 0.87 for CoGM, 0.91 for UWM, 0.60 for MWM, 0.93 for
basal ganglia and thalami, 0.87 for brainstem, 0.94 for cerebellum, 0.86 for ventricles, 0.82 for CSF. The obtained
average MHDs were 0.48 mm, 0.44 mm, 3.09 mm, 0.39 mm, 0.62 mm, 0.35 mm, 1.75 mm, 1.13 mm, for each
The proposed methods achieved high segmentation accuracy for all tissues, except for MWM, and it provides a
tool for quantification of brain tissue volumes in axial MRI scans of preterm born infants.