Self-supervised learning method for carotid plaque segmentation from B-mode ultrasound images, designed to reduce the amount of expert-labeled data needed to train a segmentation network. A level-set-and-least-squares-based deformation procedure synthesizes registration image pairs from unlabeled carotid ultrasound images, and a spatial-transformer-based registration pretext task pretrains a Stacked U-Net (Su-Net) to focus on plaque-contour features before fine-tuning on a small labeled dataset for total plaque area (TPA) segmentation. Evaluated on carotid ultrasound datasets from two different institutions and countries, the method showed robust generalization when trained with only a small number of labeled images.

Dilated U-Net model for fully automatic segmentation of the intima-media complex (IMC) of the common carotid artery on longitudinal B-mode ultrasound images, used to measure carotid intima-media thickness (cIMT) -- a standard imaging biomarker of subclinical atherosclerosis. A far-wall detection step first localizes the region of interest, and the dilated U-Net then segments the near- and far-wall IMC boundaries within it. Trained and evaluated with 5-fold cross-validation on a multicenter database of 2,176 images annotated by two experts, the method reached a mean absolute thickness difference of under 120 micrometres versus the reference annotations -- smaller than the approximately 180-micrometre inter-observer variability -- with a 98.7% fully-automatic success rate (only 1.3% of cases required manual correction).