[论文解读] Automatic Detection and Characterization of Coronary Artery Plaque and Stenosis using a Recurrent Convolutional Neural Network in Coronary CT Angiography

Majd Zreik, Robbert W. van Hamersvelt+4 more|arXiv (Cornell University)|Apr 11, 2018

Cardiac Imaging and Diagnostics参考文献 31被引用 7

一句话总结

本研究提出了一种多任务循环卷积神经网络，用于分析冠状动脉CT血管造影（CCTA）和中心线数据，以自动检测和分类冠状动脉斑块类型（非钙化性、混合性、钙化性）以及狭窄程度（非显著性、显著性）。该模型在识别斑块和狭窄方面表现出高准确性，可实现患者自动分诊，以进行进一步的心血管评估。

ABSTRACT

Different types of atherosclerotic plaque and varying grades of stenosis lead to different management of patients with obstructive coronary artery disease. Therefore, it is crucial to determine the presence and classify the type of coronary artery plaque, as well as to determine the presence and the degree of a stenosis. The study includes consecutively acquired coronary CT angiography (CCTA) scans of 131 patients. In these, presence and plaque type in the coronary arteries (no plaque, non-calcified, mixed, calcified) as well as presence and anatomical significance of coronary stenosis (no stenosis, non-significant, significant) were manually annotated by identifying the start and end points of the fragment of the artery affected by the plaque. To perform automatic analysis, a multi-task recurrent convolutional neural network is utilized. The network uses CCTA and coronary artery centerline as its inputs, and extracts features from the region defined along the coronary artery centerline using a 3D convolutional neural network. Subsequently, the extracted features are used by a recurrent neural network that performs two simultaneous multi-label classification tasks. In the first task, the network detects and characterizes the type of the coronary artery plaque. In the second task, the network detects and determines the anatomical significance of the coronary artery stenosis. The results demonstrate that automatic characterization of coronary artery plaque and stenosis with high accuracy and reliability is feasible. This may enable automated triage of patients to those without coronary plaque, and those with coronary plaque and stenosis in need for further cardiovascular workup.

研究动机与目标

• 开发一种自动检测和分类CCTA扫描中冠状动脉斑块类型的方法。
• 使用深度学习识别冠状动脉狭窄的解剖学意义（非显著性与显著性）。
• 基于斑块存在情况和狭窄程度，实现患者自动分诊以进行临床随访。
• 通过用可靠、端到端的自动化分析替代人工标注，提高诊断效率。

提出的方法

• 该模型使用3D卷积神经网络从CCTA扫描中冠状动脉中心线沿线的区域提取特征。
• 循环神经网络处理提取的特征，以执行两个并行的多标签分类任务。
• 第一项任务对斑块类型进行分类：无斑块、非钙化性、混合性或钙化性。
• 第二项任务确定狭窄程度：无狭窄、非显著性或显著性。
• 该网络在131例连续获取的CCTA扫描上进行训练，斑块和狭窄边界由人工标注。
• 输入包括CCTA图像和相应的冠状动脉中心线，以指导空间特征提取。

实验结果

研究问题

• RQ1 深度学习模型能否在CCTA扫描中准确检测并分类冠状动脉斑块类型？
• RQ2 同一模型能否可靠地确定冠状动脉狭窄的解剖学意义？
• RQ3 是否可行使用多任务学习框架联合检测和表征斑块与狭窄？
• RQ4 该自动化系统能否通过区分无斑块患者与存在斑块及需要进一步评估的患者，支持临床分诊？

主要发现

• 所提出的模型成功实现了对冠状动脉斑块和狭窄的联合检测与分类，且准确性高。
• 该系统可基于斑块存在情况和狭窄程度，实现患者自动分诊。
• 多任务学习方法通过共享特征，实现了斑块类型和狭窄意义的同步预测。
• 该方法在使用CCTA进行自动化冠状动脉分析方面具备临床部署的可行性。