Cited by 62Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and EducationMedical Imaging and Analysis

Machine-learning-based automatic identification of fetal abdominal circumference from ultrasound images

Open access
Kim, Bukweon, Kim, Kang Cheol, Park, Yejin, Kwon, Ja-Young, Jang, Jaeseong, Seo, Jin Keun·2018·Physiological Measurement

Abstract

Objective : Obstetricians mainly use ultrasound imaging for fetal biometric measurements. However, such measurements are cumbersome. Hence, there is urgent need for automatic biometric estimation. Automated analysis of ultrasound images is complicated owing to the patient-specific, operator-dependent, and machine-specific characteristics of such images. Approach : This paper proposes a method for the automatic fetal biometry estimation from 2D ultrasound data through several processes consisting of a specially designed convolutional neural network (CNN) and U-Net for each process. These machine learning techniques take clinicians’ decisions, anatomical structures, and the characteristics of ultrasound images into account. The proposed method is divided into three steps: initial abdominal circumference (AC) estimation, AC measurement, and plane acceptance checking. Main results : A CNN is used to classify ultrasound images (stomach bubble, amniotic fluid, and umbilical vein), and a Hough transform is used to obtain an initial estimate of the AC. These data are applied to other CNNs to estimate the spine position and bone regions. Then, the obtained information is used to determine the final AC. After determining the AC, a U-Net and a classification CNN are used to check whether the image is suitable for AC measurement. Finally, the efficacy of the proposed method is validated by clinical data. Significance : Our method achieved a Dice similarity metric of for AC measurement and an accuracy of 87.10% for our acceptance check of the fetal abdominal standard plane.

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Crossref · 10.1088/1361-6579/aae255

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Authors
Kim, B., Kim, K. C., Park, Y., Kwon, J.-Y., Jang, J., & Seo, J. K.
Affiliations
  • Yonsei University (KR)
  • National Institute for Mathematical Sciences (KR)
  • To whom correspondence should be addressed.
Corresponding author
Seo, Jin Keun
Venue
Physiological Measurement
Published
2018-10-01
Volume
39
Issue
10
Pages
105007
ISSN
  • 0967-3334 (ISSN-L)
  • 1361-6579
OpenAlex
W2890559952
Cited by
62
References
26
Citations by year
2025: 8 · 2024: 8 · 2023: 13 · 2022: 12 · 2021: 12 · 2020: 7 · 2019: 2
FWCI
2.63
Type
journal article
Language
en
Open access
Locations
  • Physiological Measurement (publishedVersion)
  • PubMed (publishedVersion)
  • YUHSpace (Yonsei University Medical Library) (submittedVersion, cc-by-nc-nd)
Funders
National Research Foundation of Korea
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
Keywords
fetal abdominal circumference, fetal biometry, convolutional neural networks, U-Net, Hough transform, image classification, prenatal ultrasound
MeSH terms
Abdomen, Machine Learning, Biometry, Female, Humans, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Pregnancy, Ultrasonography, Prenatal

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