3DFETUS: Deep Learning-Based Standardization of Facial Planes in 3D Ultrasound

TL;DR

This paper introduces 3DFETUS, a deep learning approach for automatic standardization of facial planes in 3D ultrasound, improving accuracy amidst noise and variability for fetal imaging.

Contribution

The paper presents a novel deep learning model, 3DFETUS, and a robust landmark-based algorithm, GT++, for accurate and automated localization of facial planes in 3D fetal ultrasound.

Findings

Achieved mean translation error of 3.21mm

Mean rotation error of 5.31 degrees

Outperformed state-of-the-art methods

Abstract

The automatic localization and standardization of anatomical planes in 3D medical imaging remains a challenging problem due to variability in object pose, appearance, and image quality. In 3D ultrasound, these challenges are exacerbated by speckle noise and limited contrast, particularly in fetal imaging. To address these challenges in the context of facial assessment, we present: 1) GT++, a robust algorithm that estimates standard facial planes from 3D US volumes using annotated anatomical landmarks; and 2) 3DFETUS, a deep learning model that automates and standardizes their localization in 3D fetal US volumes. We evaluated our methods both qualitatively, through expert clinical review, and quantitatively. The proposed approach achieved a mean translation error of 3.21 $\pm$ 1.98mm and a mean rotation error of 5.31 $\pm$ 3.945$^\circ$ per plane, outperforming other state-of-the-art methods on 3D US volumes. Clinical assessments further confirmed the effectiveness of both GT++ and 3DFETUS, demonstrating statistically significant improvements in plane estimation accuracy.