Automated Fetal Brain Extraction from Clinical Ultrasound Volumes Using 3D Convolutional Neural Networks
Moser F., Huang R., Papageorghiou AT., Papież BW., Namburete AIL.
© 2020, Springer Nature Switzerland AG. To improve the performance of most neuroimage analysis pipelines, brain extraction is used as a fundamental first step in the image processing. However, in the case of fetal brain development for routing clinical assessment, there is a need for a reliable Ultrasound (US)-specific tool. In this work we propose a fully automated CNN approach to fetal brain extraction from 3D US clinical volumes with minimal preprocessing. Our method accurately and reliably extracts the brain regardless of the large data variations in acquisition (eg. shadows, occlusions) inherent in this imaging modality. It also performs consistently throughout a gestational age range between 14 and 31 weeks, regardless of the pose variation of the subject, the scale, and even partial feature-obstruction in the image, outperforming all current alternatives.