Semi-automatic segmentation of preterm neonate ventricle system from 3D ultrasound images

3D Ultrasound (US) has been developed recently to image the intracranial ventricular system of pre-term neonates in order to monitor these patients for intraventricular hemorrhage (IVH) and the resultant dilatation of the ventricles. 3D US is capable of providing volumetric ventricle measurements, compared to clinically used 2D US, relying on linear measurements from a single slice, and visual and quantitative estimates to determine the severity of ventricular dilatation. In this work, we propose a convex optimization-based segmentation approach for 3D US images of the cerebral ventricles in preterm neonates with IVH. The proposed semi-automatic segmentation method makes use of the latest development in convex optimization techniques supervised by user interactive information. Experiments using 25 3D US images of 5 patients (5 time points for each subject) show that our proposed approach yielded a mean DSC of 78.9% compared to a manually contoured surface. This GPU-implemented semi-automated approach reduced the time required per segmentation by 1200% (mean times: 2.5 vs. 30 minutes). In addition, the intra-observer variability experiments showed that the variability introduced by the user initialization is small in terms of DSC, demonstrating a low intra-user variability.