Xiaotian Yan , Yuting Guo , Ziyi Pei , Xinyu Zhang , Jinghao Li , Zitao Zhou , Lifang Liang , Shuai Li , Peng Lun , Aimin Hao
{"title":"ICA-SAMv7:颈内动脉粗到细网络分割","authors":"Xiaotian Yan , Yuting Guo , Ziyi Pei , Xinyu Zhang , Jinghao Li , Zitao Zhou , Lifang Liang , Shuai Li , Peng Lun , Aimin Hao","doi":"10.1016/j.compmedimag.2025.102555","DOIUrl":null,"url":null,"abstract":"<div><div>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 <span><span>https://github.com/BessiePei/ICA-SAMv7</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"123 ","pages":"Article 102555"},"PeriodicalIF":5.4000,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ICA-SAMv7: Internal carotid artery segmentation with coarse to fine network\",\"authors\":\"Xiaotian Yan , Yuting Guo , Ziyi Pei , Xinyu Zhang , Jinghao Li , Zitao Zhou , Lifang Liang , Shuai Li , Peng Lun , Aimin Hao\",\"doi\":\"10.1016/j.compmedimag.2025.102555\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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 <span><span>https://github.com/BessiePei/ICA-SAMv7</span><svg><path></path></svg></span>.</div></div>\",\"PeriodicalId\":50631,\"journal\":{\"name\":\"Computerized Medical Imaging and Graphics\",\"volume\":\"123 \",\"pages\":\"Article 102555\"},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2025-04-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computerized Medical Imaging and Graphics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0895611125000643\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computerized Medical Imaging and Graphics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0895611125000643","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
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.
期刊介绍:
The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.