Automatic evaluation of fetal head biometry from ultrasound images using machine learning.

Researchers

Journal

Modalities

Models

Abstract

Ultrasound-based fetal biometric measurements, such as head circumference (HC) and biparietal diameter (BPD), are frequently used to evaluate gestational age and diagnose fetal central nervous system (CNS) pathology. Because manual measurements are operator-dependent and time-consuming, much research is being actively conducted on automated methods. However, existing automated methods are still not satisfactory in terms of accuracy and reliability, owing to diculties dealing with various artifacts in ultrasound images. This paper focuses on fetal head biometry and proposes a deep-learning-based method for estimating HC and BPD with a high degree of accuracy and reliability. Following training with a labeled dataset containing 102 ultrasound images and testing on 70 ultrasound images, the proposed method achieved a success rate of 91.43% and 100% for HC and BPD estimations, respectively, and an accuracy of 87.14% for the plane acceptance check.
© 2018 Institute of Physics and Engineering in Medicine.