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

Abstract

OBJECTIVE: 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 pathology. Because manual measurements are operator-dependent and time-consuming, much research is being actively conducted on automated methods. However, the existing automated methods are still not satisfactory in terms of accuracy and reliability, owing to difficulties dealing with various artefacts in ultrasound images.

APPROACH: Using the proposed method, a labeled dataset containing 102 ultrasound images was used for training, and validation was performed with 70 ultrasound images.

MAIN RESULTS: 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.

SIGNIFICANCE: 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.