Ultrasound Plane Pose Regression: Assessing Generalized Pose Coordinates in the Fetal Brain
Chiara Di Vece, Maela Le Lous, Brian Dromey, Francisco Vasconcelos,, Anna L David, Donald Peebles, Danail Stoyanov

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…
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Taxonomy
TopicsFetal and Pediatric Neurological Disorders · Domain Adaptation and Few-Shot Learning · Prenatal Screening and Diagnostics
