Cerebral Ventricle Segmentation from 3D Pre-term IVH Neonate MR Images Using Atlas-Based Convex Optimization

Abstract

Intraventricular hemorrhage (IVH) or brain bleeding is a common condition among pre-term infants that occurs in 15–30 % of very low birth weight preterm neonates. Infants with IVH are at risk of developing progressive dilatation of the ventricles, a pathology called hydrocephalus. The ventricular size of patients with mild enlargement of cerebral ventricles is monitored by ultrasound or MR imaging of the brain for 1–2 years, as they are at risk of developing hydrocephalus. This paper proposes an accurate and numerically efficient algorithm to the segmentation of the cerebral ventricle system of pre-term IVH neonates from 3D T1 weighted MR images. The proposed segmentation algorithm makes use of the convex optimization technique combined with the learned priors of image intensities and label probabilistic map, which is built from a multi-atlas registration scheme. The leave-one-out cross validation using 10 IVH patient T1 weighted MR images showed that the proposed method yielded a mean DSC of $83.1\,\%\pm 4.2\,\%$, a MAD of $1.0\pm 0.7$ mm, a MAXD of $11.3\pm 7.3$ mm, and a VD of $6.5\,\%\pm 6.2\,\%$, suggesting that it can be used in clinical practice for ventricle volume measurements of IVH neonate patients.