用Mamba模型提升冠脉造影中狭窄部位的自动识别准确率。
Segmentation of Coronary Artery Stenosis in X-ray Angiography using Mamba Models
- 基于U-Net架构融合Mamba和Swin Transformer模型，捕捉冠脉复杂结构。
- 最优模型F1分数达68.79%，比半监督方法提升11.8%。
- 适合医学影像分析、心血管疾病诊断领域的研究人员参考。
冠状动脉疾病是全球死亡率的主要因素之一。从X射线图像中自动识别冠状动脉狭窄对心脏病诊断至关重要。该任务因冠状动脉结构复杂、X射线图像固有噪声以及狭窄段在造影中表现为细而模糊而具有挑战性。本研究采用五种Mamba模型变体及一种Swin Transformer模型变体，均基于U-Net架构，用于冠状动脉疾病中狭窄区域的定位。最佳结果为U-Mamba BOT模型的F1分数达到68.79%，相比半监督方法提升11.8%。
原文摘要 · Abstract (English)
Coronary artery disease stands as one of the primary contributors to global mortality rates. The automated identification of coronary artery stenosis from X-ray images plays a critical role in the diagnostic process for coronary heart disease. This task is challenging due to the complex structure of coronary arteries, intrinsic noise in X-ray images, and the fact that stenotic coronary arteries appear narrow and blurred in X-ray angiographies. This study employs five different variants of the Mamba-based model and one variant of the Swin Transformer-based model, primarily based on the U-Net architecture, for the localization of stenosis in Coronary artery disease. Our best results showed an F1 score of 68.79% for the U-Mamba BOT model, representing an 11.8% improvement over the semi-supervised approach.
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