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

2025-08-19

AI Summary

Transcranial color-coded Doppler (TCCD) assessment of the Circle of Willis (CoW) suffers from high operator dependency and poor standardization. To address this, we propose AAW-YOLO—the first real-time, AI-driven CoW vessel segmentation method specifically designed for TCCD. AAW-YOLO innovatively integrates wavelet transforms to model ultrasound texture features, employs a dual-channel–spatial attention mechanism to enhance weak-signal responsiveness, and adapts the YOLO architecture for end-to-end real-time instance segmentation. Evaluated on 738 clinical TCCD frames, the model achieves Dice = 0.901, IoU = 0.823, and mAP = 0.953, with an inference latency of only 14.2 ms per frame. These results demonstrate substantial improvements in accuracy, robustness, and speed over prior approaches, significantly lowering the technical barrier for clinical use and enabling practical deployment in routine neurovascular ultrasound workflows.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

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.

Problem

• Real-time segmentation of brain vessels in TCCD imaging
• Reducing operator dependence for cerebrovascular ultrasound assessment
• Automated Circle of Willis identification for stroke risk screening

Innovation

• Attention-Augmented Wavelet YOLO network
• Real-time brain vessel segmentation
• AI-powered TCCD auto-segmentation system