通过曲线特征重建提升冠状动脉狭窄分级准确率
Clinical Risk-Aware Multi-Level Grading for Coronary Artery Stenosis through Curved Feature Reconstruction
- 利用血管曲线先验实现CCTA与3D SCPR图像点对点特征对齐融合
- 在自建数据集上模型分级准确率显著优于现有方法
- 引入临床风险感知损失，使算法更贴近实际诊疗需求
开发冠状动脉狭窄的多级分级模型对冠心病诊断具有重要意义。然而，设计有效的深度学习算法面临诸多挑战：单独使用CCTA或3D SCPR图像存在固有缺陷——CCTA因血管迂曲难以分析，3D SCPR易出现异常畸变影响分级准确性。此外，不同狭窄程度对应不同临床风险，如何将此关联融入算法尚不明确。为此，我们提出曲线特征重建（CFR）模块，以血管曲线为先验，采用点对点对应策略，精准对齐并融合3D SCPR与CCTA图像特征；同时引入临床风险感知（CR）损失，使网络训练融入临床风险相关性，提升与临床诊断的一致性。在自建数据集上的实验结果表明，本方法显著优于其他方法，消融实验也验证了各设计的有效性。
原文摘要 · Abstract (English)
Developing a multi-level grading model for coronary artery stenosis holds great clinical significance for the diagnosis of coronary artery disease. However, designing an effective multi-level deep learning algorithm faces significant challenges. Specifically, utilizing CCTA or 3D SCPR images alone presents inherent shortcomings: CCTA images are difficult to analyze due to the tortuous paths of blood vessels, while 3D SCPR images are prone to abnormal distortions that hinder accurate grading. Furthermore, different stenosis grades are associated with varying clinical risks, and incorporating this association into the algorithm is non-trivial. To address the former problems, we propose the Curved Feature Reconstruction (CFR) module, which uses vessel curves as prior and employs a point-by-point correspondence strategy to precisely align and fuse features from both 3D SCPR and CCTA images. Meanwhile, a Clinical Risk-Aware (CR) Loss is employed to introduce clinical risk relevance into the network training so that the algorithm can better align with the clinical diagnosis. The experimental results on a in-house dataset reveal that our approach significantly outperforms other methods, and several ablation studies also demonstrate the effectiveness of our proposed designs.
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