标题： 一种用于经颅彩色多普勒实时脑血管分割的新型注意力增强小波YOLO系统 显示英文标题

标题： A Novel Attention-Augmented Wavelet YOLO System for Real-time Brain Vessel Segmentation on Transcranial Color-coded Doppler

摘要： 大脑Willis环（CoW），对于确保大脑持续的血液流动至关重要，与缺血性中风密切相关。 对CoW的准确评估对于识别高风险人群和指导适当的临床管理非常重要。 在现有的成像方法中，经颅彩色多普勒（TCCD）由于无辐射、成本低和易于获取而具有独特的优势。 然而，可靠的TCCD评估高度依赖于操作员的专业技能来识别解剖标志并进行精确的角度校正，这限制了其广泛应用。 为了解决这一挑战，我们提出了一种基于人工智能的实时CoW自动分割系统，能够高效地捕捉脑动脉。 此前没有研究探讨过使用TCCD进行人工智能驱动的脑血管分割。 在本工作中，我们介绍了一种针对TCCD数据设计的新型注意力增强小波YOLO（AAW-YOLO）网络，旨在为CoW中的脑血管分割提供实时指导。 我们前瞻性地收集了包含738个注释帧和3,419个标记的动脉实例的TCCD数据，以建立一个高质量的数据集用于模型训练和评估。 所提出的AAW-YOLO在分割同侧和对侧CoW血管方面表现出色，平均Dice得分为0.901，IoU为0.823，精度为0.882，召回率为0.926，mAP为0.953，每帧推理速度为14.199毫秒。 该系统提供了一个实用的解决方案，以减少对TCCD为基础的脑血管筛查中操作员经验的依赖，具有在常规临床工作流程和资源匮乏环境中的潜在应用。 未来的研究将探索双侧建模和更大规模的验证。 显示英文摘要

摘要： The Circle of Willis (CoW), vital for ensuring consistent blood flow to the brain, is closely linked to ischemic stroke. Accurate assessment of the CoW is important for identifying individuals at risk and guiding appropriate clinical management. Among existing imaging methods, Transcranial Color-coded Doppler (TCCD) offers unique advantages due to its radiation-free nature, affordability, and accessibility. However, reliable TCCD assessments depend heavily on operator expertise for identifying anatomical landmarks and performing accurate angle correction, which limits its widespread adoption. To address this challenge, we propose an AI-powered, real-time CoW auto-segmentation system capable of efficiently capturing cerebral arteries. No prior studies have explored AI-driven cerebrovascular segmentation using TCCD. In this work, we introduce a novel Attention-Augmented Wavelet YOLO (AAW-YOLO) network tailored for TCCD data, designed to provide real-time guidance for brain vessel segmentation in the CoW. We prospectively collected TCCD data comprising 738 annotated frames and 3,419 labeled artery instances to establish a high-quality dataset for model training and evaluation. The proposed AAW-YOLO demonstrated strong performance in segmenting both ipsilateral and contralateral CoW vessels, achieving an average Dice score of 0.901, IoU of 0.823, precision of 0.882, recall of 0.926, and mAP of 0.953, with a per-frame inference speed of 14.199 ms. This system offers a practical solution to reduce reliance on operator experience in TCCD-based cerebrovascular screening, with potential applications in routine clinical workflows and resource-constrained settings. Future research will explore bilateral modeling and larger-scale validation.