LT-YOLO: Long-term Temporal Enhanced YOLO for Stenosis Detection on Invasive Coronary Angiography

Jiaxin Li ^1,2* Xiang Tang ² Xuesong Wang ³

• ¹ Sun Yat-sen University, Guangzhou, China
• ² School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
• ³ College of Mining Engineering, University of Science and Technology Liaoning, Anshan, China

Coronary artery stenosis detection on Invasive Coronary Angiography plays a pivotal role in computer-aided diagnosis and treatment. However, it faces the challenge of stenotic morphology confusion stemmed from coronary-background similarity, varied morphology and small area of the stenosis. Furthermore, existing automated methods ignore the long-temporal information mining. To address these limitations, this paper proposes a Long-term Temporal Enhanced YOLO for automatic stenosis detection and assessment in Invasive Coronary Angiography. Our approach integrates longterm temporal information and spatial information for stenosis detection with state space models and YOLOv8. First, a spatial-aware backbone based on dynamic transformer and C2f Convolution of YOLOv8 combines the local and global feature extraction to distinguish the coronary regions from the background. Second, a spatial-temporal multi-level fusion neck integrates the long-term temporal and spatial features to handle varied stenotic morphology. Third, a detail-aware detection head leverages low-level information for accurate identification of small stenosis. Extensive experiments on 350 ICA video sequences demonstrate the model's superior performance over seven state-of-the-art methods, particularly in detecting small stenosis (percentage less than 50%), which was previously underexplored.