Carotid artery wall segmentation in ultrasound image sequences using a deep convolutional neural network
This provides a robust tool for clinical practice in cardiovascular disease diagnosis by automating a manual measurement task.
The study tackled automatic segmentation of carotid artery walls in ultrasound images to measure intima-media thickness, achieving a mean absolute difference under 120 μm (less than inter-observer variability) and a 98.7% success rate with minimal manual correction.
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.