Geometric Transformation Uncertainty for Improving 3D Fetal Brain Pose Prediction from Freehand 2D Ultrasound Videos

TL;DR

This paper introduces QAERTS, an uncertainty-aware deep learning model that improves 3D fetal brain plane localization from 2D ultrasound images by explicitly modeling geometric transformation uncertainties, achieving higher accuracy and robustness.

Contribution

The paper presents a novel uncertainty-aware multi-head network for 3D plane localization in fetal ultrasound, outperforming state-of-the-art methods with fewer parameters and enhanced noise robustness.

Findings

9% improvement in plane angle accuracy

8% enhancement in image quality (NCC)

5× fewer parameters than ensemble methods

Abstract

Accurately localizing two-dimensional (2D) ultrasound (US) fetal brain images in the 3D brain, using minimal computational resources, is an important task for automated US analysis of fetal growth and development. We propose an uncertainty-aware deep learning model for automated 3D plane localization in 2D fetal brain images. Specifically, a multi-head network is trained to jointly regress 3D plane pose from 2D images in terms of different geometric transformations. The model explicitly learns to predict uncertainty to allocate higher weight to inputs with low variances across different transformations to improve performance. Our proposed method, QAERTS, demonstrates superior pose estimation accuracy than the state-of-the-art and most of the uncertainty-based approaches, leading to 9% improvement on plane angle (PA) for localization accuracy, and 8% on normalized cross-correlation (NCC) for sampled image quality. QAERTS also demonstrates efficiency, containing 5$\times$ fewer parameters than ensemble-based approach, making it advantageous in resource-constrained settings. In addition, QAERTS proves to be more robust to noise effects observed in freehand US scanning by leveraging rotational discontinuities and explicit output uncertainties.