ICA-SAMv7: Internal carotid artery segmentation with coarse to fine network

Internal carotid artery (ICA) stenosis is a life-threatening occult disease. Using Computed Tomography Angiography (CTA) to examine vascular lesions such as calcified and non-calcified plaques in cases of carotid artery stenosis is a necessary clinical step in formulating the correct treatment plan. Segment Anything Model (SAM) has shown promising performance in image segmentation tasks, but it performs poorly for carotid artery segmentation. Due to the small size of the calcification and the overlapping between the lumen and calcification, these challenges lead to issues such as mislabeling and boundary fragmentation, as well as high training costs. To address these problems, we propose a two-stage Carotid Artery lesion segmentation method called ICA-SAMv7, which performs coarse and fine segmentation based on the YOLOv7 and SAM model. Specifically, in the first stage (ICA-YOLOv7), we utilize YOLOv7 for coarse vessel recognition, introducing connectivity enhancement to improve accuracy and achieve precise localization of small target carotid artery. In the second stage (ICA-SAM), we enhance SAM through data augmentation and an efficient parameter fine-tuning strategy. This improves the segmentation accuracy of fine-grained lesions in blood vessels while saving training costs. Ultimately, the accuracy of lesion segmentation under the SAM model was increased from the original 48.62% to 83.69%. Extensive comparative experiments have demonstrated the outstanding performance of our algorithm. Our codes can be found at https://github.com/BessiePei/ICA-SAMv7.