The objective of this study is the segmentation of the intima-media complex of the common carotid artery, on longitudinal ultrasound images, to measure its thickness. We propose a fully automatic region-based segmentation method, involving a supervised region-based deep-learning approach based on a dilated U-net network. It was trained and evaluated using a 5-fold cross-validation on a multicenter database composed of 2176 images annotated by two experts. The resulting mean absolute difference (<120 um) compared to reference annotations was less than the inter-observer variability (180 um). With a 98.7% success rate, i.e., only 1.3% cases requiring manual correction, the proposed method has been shown to be robust and thus may be recommended for use in clinical practice.
@article{arxiv.2201.12152,
title = {Carotid artery wall segmentation in ultrasound image sequences using a deep convolutional neural network},
author = {Nolann Lainé and Guillaume Zahnd and Herv é Liebgott and Maciej Orkisz},
journal= {arXiv preprint arXiv:2201.12152},
year = {2022}
}