Automatic estimation of fetal abdominal circumference from ultrasound images

Jaeseong Jang, Yejin Park, Bukweon Kim, Sung Min Lee, Ja Young Kwon, Jin Keun Seo

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


Ultrasound diagnosis is routinely used in obstetrics and gynecology for fetal biometry, and owing to its time-consuming process, there has been a great demand for automatic estimation. However, the automated analysis of ultrasound images is complicated because they are patient specific, operator dependent, and machine specific. Among various types of fetal biometry, the accurate estimation of abdominal circumference (AC) is especially difficult to perform automatically because the abdomen has low contrast against surroundings, nonuniform contrast, and irregular shape compared to other parameters. We propose a method for the automatic estimation of the fetal AC from two-dimensional ultrasound data through a specially designed convolutional neural network (CNN), which takes account of doctors’ decision process, anatomical structure, and the characteristics of the ultrasound image. The proposed method uses CNN to classify ultrasound images (stomach bubble, amniotic fluid, and umbilical vein) and Hough transformation for measuring AC. We test the proposed method using clinical ultrasound data acquired from 56 pregnant women. Experimental results show that, with relatively small training samples, the proposed CNN provides sufficient classification results for AC estimation through the Hough transformation. The proposed method automatically estimates AC from ultrasound images. The method is quantitatively evaluated and shows stable performance in most cases and even for ultrasound images deteriorated by shadowing artifacts. As a result of experiments for our acceptance check, the accuracies are 0.809 and 0.771 with expert 1 and expert 2, respectively, whereas the accuracy between the two experts is 0.905. However, for cases of oversized fetus, when the amniotic fluid is not observed or the abdominal area is distorted, it could not correctly estimate AC.

Original languageEnglish
Pages (from-to)1512-1520
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
Issue number5
Publication statusPublished - 2018

Bibliographical note

Funding Information:
Manuscript received August 1, 2017; revised November 3, 2017; accepted November 11, 2017. Date of publication November 21, 2017; date of current version August 31, 2018. This work was supported in part by the National Institute for Mathematical Sciences grant funded by the Korean Government (A21300000) and in part by the National Research Foundation of Korea under Grant 2015R1A5A1009350 and Grant 2017R1A2B20005661. (Corresponding author: Ja-Young Kwon.) J. Jang is with the Division of Strategic Research, National Institute for Mathematical Sciences, Daejeon 34047, South Korea (e-mail:

Publisher Copyright:
© 2017 IEEE.

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management


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