Objective <p>Artificial intelligence models are increasingly used for stroke risk evaluation and clinical decision-making. However, the scarcity of expert masks, low contrast, and noise in ultrasound images affect segmentation performance.</p> Methods <p>Our study innovatively integrated an adversarial PatchGAN discriminator into a batch-normalized semi-supervised U-Net generator to enhance carotid plaque segmentation in ultrasound images with limited expert annotation. The discriminator provides patch-level localized feedback to enhance boundary delineation and structural consistency of the predicted masks. The model was trained using a hybrid loss function and self-training strategy. The performance of our framework was compared with a fine-tuned semi-supervised U-Net model. Both frameworks were trained under identical experimental settings using 30% of the labeled data; the remaining unlabeled images were utilized to generate pseudo-labels for data augmentation. A confidence threshold of 0.7 was applied to filter unreliable predictions.</p> Results <p>We conducted experiments on a dataset of 970 internal carotid artery ultrasound images from the Imperial College of London, UK. The experimental results showed that the proposed framework achieved a Dice coefficient of 86.12 and a Jaccard index of 75.66, outperforming the baseline U-Net (84.22 and 73.12, respectively).</p> Conclusions <p>The integration of patch-level adversarial feedback into a semi-supervised framework enhanced the segmentation accuracy and improved the reliability of pseudo-labels under limited supervision. The results were comparable to those of state-of-the-art deep learning models, which require a large pool of labeled data.</p>