  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.
