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.

我们提出了一种基于不确定性的深度学习模型，用于自动定位二维超声胎儿脑图像在三维脑中的平面位置。该方法通过学习显式预测不确定性，从而提高输入的权重，并在不同几何变换下改善性能。与现有方法相比，我们的方法在平面角度定位准确度上提高了 9%，在采样图像质量的归一化互相关上提高了 8%。此外，我们的方法在资源受限环境下具有更高的效率，参数量比集成方法少 5 倍，同时利用旋转不连续性和输出不确定性提高了对自由扫描噪声的鲁棒性。