Significance: Detection and characterization of coronary atherosclerotic plaques often need reviews of a large number of optical coherence tomography (OCT) imaging slices to make a clinical decision. However, it is a challenge to manually review all the slices and consider the interrelationship between adjacent slices. Approach: Inspired by the recent success of deep convolutional network on the classification of medical images, we proposed a ResNet-3D network for classification of coronary plaque calcification in OCT pullbacks. The ResNet-3D network was initialized with a trained ResNet-50 network and a three-dimensional convolution filter filled with zeros padding and non-zeros padding with a convolutional filter. To retrain ResNet-50, we used a dataset of ∼4860 OCT images, derived by 18 entire pullbacks from different patients. In addition, we investigated a two-phase training method to address the data imbalance. For an improved performance, we evaluated different input sizes for the ResNet-3D network, such as 3, 5, and 7 OCT slices. Furthermore, we integrated all ResNet-3D results by majority voting. Results: A comparative analysis proved the effectiveness of the proposed ResNet-3D networks against ResNet-2D network in the OCT dataset. The classification performance (F1-scores = 94 % for non-zeros padding and F1-score = 96 % for zeros padding) demonstrated the potential of convolutional neural networks (CNNs) in classifying plaque calcification. Conclusions: This work may provide a foundation for further work in extending the CNN to voxel segmentation, which may lead to a supportive diagnostic tool for assessment of coronary plaque vulnerability.

中文翻译：

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使用 3D 深度神经网络自动分类 OCT 回撤中的冠状动脉斑块钙化。

意义：冠状动脉粥样硬化斑块的检测和表征通常需要对大量光学相干断层扫描 (OCT) 成像切片进行审查才能做出临床决策。然而，手动检查所有切片并考虑相邻切片之间的相互关系是一个挑战。方法：受最近深度卷积网络在医学图像分类方面取得的成功启发，我们提出了一个 ResNet-3D 网络，用于分类 OCT 回撤中的冠状动脉斑块钙化。ResNet-3D 网络使用经过训练的 ResNet-50 网络和一个填充零填充和非零填充的三维卷积滤波器初始化，并使用卷积滤波器进行填充。为了重新训练 ResNet-50，我们使用了一个包含 4860 张 OCT 图像的数据集，这些图像由来自不同患者的 18 个完整的回撤得到。此外，我们研究了一种两阶段训练方法来解决数据不平衡问题。为了提高性能，我们评估了 ResNet-3D 网络的不同输入大小，例如 3、5 和 7 个 OCT 切片。此外，我们通过多数投票整合了所有 ResNet-3D 结果。结果：对比分析证明了所提出的 ResNet-3D 网络对 OCT 数据集中的 ResNet-2D 网络的有效性。分类性能（非零填充的 F1 分数 = 94 % 和零填充的 F1 分数 = 96 %）证明了卷积神经网络 (CNN) 在分类斑块钙化方面的潜力。结论：这项工作可能为将 CNN 扩展到体素分割的进一步工作提供基础，这可能会导致评估冠状动脉斑块脆弱性的支持性诊断工具。