Automated fetal brain extraction from clinical Ultrasound volumes using 3D Convolutional Neural Networks

TL;DR

This paper introduces a fully automated 3D CNN method for fetal brain extraction from ultrasound volumes, demonstrating high accuracy and robustness across various gestational ages, poses, and image qualities, surpassing existing methods.

Contribution

The study presents a novel deep learning approach specifically tailored for fetal brain extraction in ultrasound imaging, addressing challenges of variability and partial obstructions.

Findings

Outperforms existing methods in accuracy and reliability.

Effective across a wide gestational age range (14-31 weeks).

Robust to pose variations, scale, and partial obstructions.

Abstract

To improve the performance of most neuroimiage analysis pipelines, brain extraction is used as a fundamental first step in the image processing. But in the case of fetal brain development, there is a need for a reliable US-specific tool. In this work we propose a fully automated 3D 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 variation 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.