In obstetric ultrasound (US) scanning, the learner's ability to mentally build a three-dimensional (3D) map of the fetus from a two-dimensional (2D) US image represents a significant challenge in skill acquisition. We aim to build a US plane localization system for 3D visualization, training, and guidance without integrating additional sensors. This work builds on top of our previous work, which predicts the six-dimensional (6D) pose of arbitrarily oriented US planes slicing the fetal brain with respect to a normalized reference frame using a convolutional neural network (CNN) regression network. Here, we analyze in detail the assumptions of the normalized fetal brain reference frame and quantify its accuracy with respect to the acquisition of transventricular (TV) standard plane (SP) for fetal biometry. We investigate the impact of registration quality in the training and testing data and its subsequent effect on trained models. Finally, we introduce data augmentations and larger training sets that improve the results of our previous work, achieving median errors of 2.97 mm and 6.63 degrees for translation and rotation, respectively.

翻译：在产科超声扫描中，学习者从二维超声图像中构建胎儿三维空间心智表征的能力是技能习得的重大挑战。本研究旨在构建无需集成额外传感器的超声平面定位系统，用于三维可视化、训练与引导。该工作基于我们先前的研究，其通过卷积神经网络回归网络预测任意方向切割胎儿脑的超声平面相对于归一化参考系的六维位姿。本文深入分析了归一化胎儿脑参考系的假设条件，并量化了其相对于用于胎儿生物测量的经脑室标准平面采集的精度。我们研究了配准质量对训练与测试数据的影响及其对训练模型的后续效应。最后，通过引入数据增强和扩大训练集，改进了先前工作的结果，使平移和旋转的中位误差分别达到2.97毫米和6.63度。