ICA-SAMv7:颈内动脉粗到细网络分割

IF 5.4 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Xiaotian Yan , Yuting Guo , Ziyi Pei , Xinyu Zhang , Jinghao Li , Zitao Zhou , Lifang Liang , Shuai Li , Peng Lun , Aimin Hao
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引用次数: 0

摘要

颈内动脉狭窄是一种危及生命的隐匿性疾病。应用ct血管造影(CTA)检查颈动脉狭窄患者的血管病变,如钙化斑块和非钙化斑块,是制定正确治疗方案的必要临床步骤。分割任意模型(SAM)在图像分割任务中表现出良好的性能,但在颈动脉分割任务中表现不佳。由于钙化的小尺寸以及管腔和钙化之间的重叠,这些挑战导致了诸如错误标记和边界碎片化等问题,以及高训练成本。为了解决这些问题,我们提出了一种两阶段的颈动脉病变分割方法ICA-SAMv7,该方法基于YOLOv7和SAM模型进行粗分割和细分割。具体而言,在第一阶段(ICA-YOLOv7)中,我们利用YOLOv7进行粗血管识别,引入连接增强以提高准确性并实现小目标颈动脉的精确定位。在第二阶段(ICA-SAM)中,我们通过数据增强和有效的参数微调策略来增强SAM。这提高了血管细粒度病变的分割精度,同时节省了训练成本。最终,SAM模型下病灶分割的准确率由原来的48.62%提高到83.69%。大量的对比实验证明了该算法的优异性能。我们的代码可以在https://github.com/BessiePei/ICA-SAMv7上找到。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
CiteScore
10.70
自引率
3.50%
发文量
71
审稿时长
26 days
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.
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