Background and objective

Coronary artery disease, which is mostly caused by atherosclerotic narrowing of the coronary artery lumen, is a leading cause of death. Coronary angiography is the standard method to estimate the severity of coronary artery stenosis, but is frequently limited by intra- and inter-observer variations. We propose a deep-learning algorithm that automatically recognizes stenosis in coronary angiographic images.

Methods

The proposed method consists of key frame detection, deep learning model training for classification of stenosis on each key frame, and visualization of the possible location of the stenosis. Firstly, we propose an algorithm that automatically extracts key frames essential for diagnosis from 452 right coronary artery angiography movie clips. Our deep learning model is then trained with image-level annotations to classify the areas narrowed by over 50 %. To make the model focus on the salient features, we apply a self-attention mechanism. The stenotic locations are visualized using the activated area of feature maps with gradient-weighted class activation mapping.

Results

The automatically detected key frame was very close to the manually selected key frame (average distance (1.70 ± 0.12) frame per clip). The model was trained with key frames on internal datasets, and validated with internal and external datasets. Our training method achieved high frame-wise area-under-the-curve of 0.971, frame-wise accuracy of 0.934, and clip-wise accuracy of 0.965 in the average values of cross-validation evaluations. The external validation results showed high performances with the mean frame-wise area-under-the-curve of (0.925 and 0.956) in the single and ensemble model, respectively. Heat map visualization shows the location for different types of stenosis in both internal and external data sets. With the self-attention mechanism, the stenosis could be precisely localized, which helps to accurately classify the stenosis by type.

Conclusions

Our automated classification algorithm could recognize and localize coronary artery stenosis highly accurately. Our approach might provide the basis for a screening and assistant tool for the interpretation of coronary angiography.

中文翻译：

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使用卷积神经网络从冠状动脉造影自动识别狭窄

背景和目标

：冠状动脉疾病（主要由冠状动脉腔的动脉粥样硬化变窄引起）是导致死亡的主要原因。冠状动脉造影是评估冠状动脉狭窄严重程度的标准方法，但通常受观察者间和观察者间差异的限制。我们提出了一种深度学习算法，可以自动识别冠状动脉造影图像中的狭窄。

方法

：建议的方法包括关键帧检测，对每个关键帧上的狭窄进行分类的深度学习模型训练，以及可视化狭窄的可能位置。首先，我们提出了一种算法，该算法会自动从452个右冠状动脉血管造影影片剪辑中提取诊断所必需的关键帧。然后，我们的深度学习模型将通过图像级注释进行训练，以对缩小幅度超过50％的区域进行分类。为了使模型专注于显着特征，我们应用了一种自我关注机制。使用带有梯度加权类激活映射的特征图的激活区域，可以看到狭窄的位置。

结果

：自动检测到的关键帧与手动选择的关键帧非常接近（每个剪辑的平均距离（1.70±0.12）帧）。该模型使用内部数据集中的关键帧进行训练，并使用内部和外部数据集进行了验证。我们的训练方法在交叉验证评估的平均值中实现了0.971的高框式曲线下面积，0.934的框式精度和0.965的限幅式精度。外部验证结果表明，在单模型和集成模型中，曲线的平均框架下曲线下面积分别为（0.925和0.956），具有较高的性能。热图可视化显示内部和外部数据集中不同类型狭窄的位置。利用自我注意机制，可以精确地定位狭窄，

结论

：我们的自动分类算法可以高度准确地识别和定位冠状动脉狭窄。我们的方法可能为解释冠状动脉造影的筛查和辅助工具提供基础。