Ultrasound Plane Pose Regression: Assessing Generalized Pose Coordinates in the Fetal Brain

TL;DR

This paper develops a CNN-based system for localizing ultrasound planes in fetal brain imaging, improving accuracy in 3D visualization and guidance without extra sensors, by analyzing assumptions, data quality, and augmentations.

Contribution

It introduces enhanced data augmentation and larger training sets to improve CNN-based fetal ultrasound plane pose regression accuracy.

Findings

Median translation error of 2.97 mm

Median rotation error of 6.63 degrees

Impact of registration quality on model performance

Abstract

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.