Main Contributor: Zachary Szentimrey
Our work is to automate neonatal ventricle segmentation from 3D ultrasound images using deep learning segmentation models. We use both labelled and unlabelled data to assist in 3D ultrasound segmentation. The purpose of our work is to provide clinicians with quantitative information on ventricle size changes in neonates with intraventricular bleeding to improve patient outcome.