Department of Cardiology, West China Hospital, Sichuan University, No. 37 Guoxue Street, Chengdu, 610041, People's Republic of China.
Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu, 610065, People's Republic of China.
Eur Radiol. 2022 Sep;32(9):6037-6045. doi: 10.1007/s00330-022-08761-z. Epub 2022 Apr 8.
Coronary computed tomography angiography (CCTA) has rapidly developed in the coronary artery disease (CAD) field. However, manual coronary artery tree segmentation and reconstruction are time-consuming and tedious. Deep learning algorithms have been successfully developed for medical image analysis to process extensive data. Thus, we aimed to develop a deep learning tool for automatic coronary artery reconstruction and an automated CAD diagnosis model based on a large, single-centre retrospective CCTA cohort.
Automatic CAD diagnosis consists of two subtasks. One is a segmentation task, which aims to extract the region of interest (ROI) from original images with U-Net. The second task is an identification task, which we implemented using 3DNet. The coronary artery tree images and clinical parameters were input into 3DNet, and the CAD diagnosis result was output.
We built a coronary artery segmentation model based on CCTA images with the corresponding labelling. The segmentation model had a mean Dice value of 0.771 ± 0.021. Based on this model, we built an automated diagnosis model (classification model) for CAD. The average accuracy and area under the receiver operating characteristic curve (AUC) were 0.750 ± 0.056 and 0.737, respectively.
Herein, using a deep learning algorithm, we realized the rapid classification and diagnosis of CAD from CCTA images in two steps. Our deep learning model can automatically segment the coronary artery quickly and accurately and can deliver a diagnosis of ≥ 50% coronary artery stenosis. Artificial intelligence methods such as deep learning have the potential to elevate the efficiency in CCTA image analysis considerably.
• The deep learning model rapidly achieved a high Dice value (0.771 ± 0.0210) in the autosegmentation of coronary arteries using CCTA images. • Based on the segmentation model, we built a CAD autoclassifier with the 3DNet algorithm, which achieved a good diagnostic performance (AUC) of 0.737. • The deep neural network could be used in the image postprocessing of coronary computed tomography angiography to achieve a quick and accurate diagnosis of CAD.
冠状动脉计算机断层血管造影术（CCTA）在冠心病（CAD）领域迅速发展。然而，手动冠状动脉树的分割和重建既耗时又乏味。深度学习算法已成功应用于医学图像分析，以处理大量数据。因此，我们旨在基于大型单中心回顾性 CCTA 队列，开发一种用于自动冠状动脉重建和自动 CAD 诊断模型的深度学习工具。
自动 CAD 诊断包括两个子任务。一个是分割任务，旨在使用 U-Net 从原始图像中提取感兴趣区域（ROI）。第二个任务是识别任务，我们使用 3DNet 来实现。冠状动脉树图像和临床参数输入到 3DNet 中，输出 CAD 诊断结果。
我们构建了一个基于 CCTA 图像和相应标注的冠状动脉分割模型。分割模型的平均 Dice 值为 0.771 ± 0.021。在此模型基础上，我们构建了 CAD 的自动诊断模型（分类模型）。平均准确率和接收者操作特征曲线下的面积（AUC）分别为 0.750 ± 0.056 和 0.737。
本文使用深度学习算法，通过两步实现了从 CCTA 图像快速分类和诊断 CAD。我们的深度学习模型可以快速、准确地自动分割冠状动脉，并可以诊断≥50%的冠状动脉狭窄。深度学习等人工智能方法有可能极大地提高 CCTA 图像分析的效率。
深度学习模型在使用 CCTA 图像进行自动冠状动脉分割时迅速达到了较高的 Dice 值（0.771 ± 0.021）。
基于分割模型，我们使用 3DNet 算法构建了 CAD 自动分类器，其诊断性能（AUC）良好，为 0.737。
深度神经网络可用于冠状动脉计算机断层血管造影术的图像后处理，实现 CAD 的快速准确诊断。
