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Automatic evaluation of fetal head biometry from ultrasound images using machine learning
- Publication Year :
- 2018
-
Abstract
- Ultrasound-based fetal biometric measurements, such as head circumference (HC) and biparietal diameter (BPD), are commonly used to evaluate the gestational age and diagnose fetal central nervous system (CNS) pathology. Since manual measurements are operator-dependent and time-consuming, there have been numerous researches on automated methods. However, existing automated methods still are not satisfactory in terms of accuracy and reliability, owing to difficulties in dealing with various artifacts in ultrasound images. This paper focuses on fetal head biometry and develops a deep-learning-based method for estimating HC and BPD with a high degree of accuracy and reliability. The proposed method effectively identifies the head boundary by differentiating tissue image patterns with respect to the ultrasound propagation direction. The proposed method was trained with 102 labeled data set and tested to 70 ultrasound images. We achieved a success rate of 92.31% for HC and BPD estimations, and an accuracy of 87.14 % for the plane acceptance check.
- Subjects :
- Biometry
Biometrics
Physiology
Computer science
Cephalometry
Biomedical Engineering
Biophysics
FOS: Physical sciences
Ultrasonography, Prenatal
Machine Learning
Automation
Physiology (medical)
Image Processing, Computer-Assisted
Humans
Fetal head
Reliability (statistics)
Biparietal diameter
business.industry
Ultrasound
Gestational age
Pattern recognition
Physics - Medical Physics
Head circumference
Regression Analysis
Medical Physics (physics.med-ph)
Artificial intelligence
business
Head
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....c13359c53fca7be4411d300902338d7e