Automated Fetal Head Classification and Segmentation Using Ultrasound Video

Abstract

During pregnancy, fetal ultrasound provides essential insight into a baby's growth and development. In this ultrasound, accurate assessment of fetal head biometry is critical to the clinical management of pregnancy. Current methodologies used for fetal head biometry heavily rely upon sonographer skills and experience to locate a baby's head. In this paper a novel approach is proposed to automate the fetal head biometry using live ultrasound feed; which is also capable of tackling low abdominal contrast against surroundings. Proposed model is trained on ALEXNET and UNET for classification and segmentation of headframes respectively from ultrasound video. To compute biparietal diameter (BPD) and head circumference (HC); which are essential requirements to compute gestational age, an ellipse is drawn on the contour of the annotated segmented fetal head. It should be noted that to validate gestational age estimate, ellipse are drawn on multiple best classified headframes obtained using ALEXNET. The proposed system is able to estimate gestational age within clinically acceptable ± one week of observed gestational age with an accuracy of 96%. Moreover, it uses robust machine vision features to reduce the sonographer's interaction with the system, thus reducing the overall procedure time and making it independent of sonographer's skill.