A hybrid pipeline for carotid artery segmentation using YOLOv11n and contour models

Carotid artery segmentation is critical for determining the degree of vascular disease, and for recommending treatment options. Early detection of carotid atherosclerosis is critical for preventing stroke. Stroke-related brain damage can cause deficits in speech or vision, and large strokes can be fatal. However, automatic segmentation of the carotid artery lumen remains difficult due to the low quality of US images, and the existence of stenosis, jugular veins, and abnormalities in carotid images. This article presents a hybrid pipeline for segmenting both carotid transverse and longitudinal lumens without any user interaction. This hybrid pipeline starts with automatically localizing the carotid artery lumen in the transverse and longitudinal sections via YOLOv11n. Then, a multistage preprocessing framework was applied to the transverse section before its lumen was segmented by the active contour. For the longitudinal section, an automated padded mask was generated to guarantee reliable initialization for Chan-Vese level-set evolution. A paired t test validated the relevance of the proposed modules (p < 0.0001). The proposed multiphase segmentation pipeline achieved a Dice index and accuracy of 94.9% and 97.7%, respectively, for the longitudinal section and 90.8% and 99.6%, respectively, for the transverse section. A comprehensive ablation analysis has shown that numerical stability depends on the YOLOv11n localization phase. The system attained near real-time inference for the carotid transverse section (< 1 s) despite being evaluated on low-end hardware, demonstrating its computational efficiency and promise for clinical integration.