Fully-Automated Identification and Segmentation of Aortic Lumen from Fetal Ultrasound Images

Abstract

Intrauterine growth restriction (IUGR) is a fetal condition that has been linked to an increase in cardiovascular mortality in the adult life. IUGR induces cardiovascular remodeling, including a decrease in aortic intima-media thickness (aIMT) which can be evaluated using fetal ultrasound imaging, potentially improving IUGR assessment and cardiovascular risk management. A necessary step for aIMT quantification is the identification of a region-of-interest (ROI) containing the lumen. This step is usually performed manually, even within the few semi-automated approaches to aIMT estimation. The aims of this study were to develop and test a fully-automated technique for lumen identification and segmentation from ultrasound images as a basis for aIMT quantification. The technique relies on convolution with a set of discriminative kernels learned from a training dataset using an AdaBoost classifier followed by segmentation based on anisotropic filtering and level-set methods. This approach was tested on 50 images acquired from 5 subjects: automatically extracted mean lumen width values were compared to reference ones manually obtained by an experienced interpreter. Results (R = 0.97) show that the proposed technique is accurate, suggesting that it could serve as a basis for fully-automated approaches to aIMT quantification.