월: 2017 3월

CNN based Automated Fetal Biometry

Ultrasound is the most commonly used tool in the field of obstetrics for the anatomical and functional surveillance of fetuses. Fetal biometry (estimation of the fetal biparietal diameter (BPD), head circumference (HC), and abdominal circumference (AC) has been known to be useful for predicting intrauterine growth restriction and fetal maturity, and for estimating gestational age. Owing to its time-consuming routine process, there has been great demand for the automatic estimation of ultrasound images in fetal biometry to improve doctors’ work flow. Unfortunately, the analysis of ultrasound images is complicated because ultrasound images are patient-specific, operator-dependent, and machine-specific. Hence, automated fetal biometry estimation must be able to handle noisy ultrasound images, which are affected by signal dropouts, artifacts, missing boundaries, attenuation, shadows, and speckle.

ac

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 developed 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. This method uses CNN to classify ultrasound images (stomach bubble, amniotic fluid, and umbilical vein) and Hough transformation for measuring AC. For details, see the paper, Automatic Estimation of Abdominal Circumference from Ultrasound Images.