  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,
non-uniform contrast, and irregular shape compared to other parameters.We
propose a method for the automatic estimation of the fetal AC from 2D
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 the expert 1 and expert 2, respectively, while 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.
