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

Authors
 Hwa Pyung Kim  ;  Sung Min Lee  ;  Ja-Young Kwon  ;  Yejin Park  ;  Kang Cheol Kim  ;  Jin Keun Seo 
Citation
 Physiological Measurement, Vol.40(6) : 65009, 2019 
Journal Title
 Physiological Measurement 
ISSN
 0967-3334 
Issue Date
2019
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.
Full Text
https://iopscience.iop.org/article/10.1088/1361-6579/ab21ac
DOI
10.1088/1361-6579/ab21ac
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Obstetrics and Gynecology (산부인과학교실) > 1. Journal Papers
Yonsei Authors
Kwon, Ja Young(권자영) ORCID logo https://orcid.org/0000-0003-3009-6325
Park, Yejin(박예진) ORCID logo https://orcid.org/0000-0002-0545-7267
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/171057
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