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

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 re...

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Bibliographic Details
Published in:Physiological measurement 2019-07, Vol.40 (6), p.065009-065009
Main Authors: Kim, Hwa Pyung, Lee, Sung Min, Kwon, Ja-Young, Park, Yejin, Kim, Kang Cheol, Seo, Jin Keun
Format: Article
Language:English
Subjects:
Automation
Biometry
Cephalometry
fetal head biometry
Head - diagnostic imaging
Humans
Image Processing, Computer-Assisted
Machine Learning
Regression Analysis
Ultrasonography, Prenatal
ultrasound
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Summary: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.
ISSN:0967-3334
1361-6579
1361-6579
DOI:10.1088/1361-6579/ab21ac