利用深度学习技术从冠状动脉造影图像中自动提取冠状动脉。

Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms.

机构信息

School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, China.

Department of Applied Computing, Michigan Technological University, Houghton, MI, USA.

出版信息

Technol Health Care. 2023;31(6):2303-2317. doi: 10.3233/THC-230278.

DOI:10.3233/THC-230278

PMID:37545276

Abstract

BACKGROUND

Accurate extraction of coronary arteries from invasive coronary angiography (ICA) images is essential for the diagnosis and risk stratification of coronary artery disease (CAD).

OBJECTIVE

In this study, a novel deep learning (DL) method is proposed for automatically extracting coronary arteries from ICA images.

METHODS

A convolutional neural network (CNN) was developed with full-scale skip connections and full-scale deep supervisions. The encoder architecture was based on the residual and inception modules to obtain multi-scale features from multiple convolutional layers with different window shapes. Transfer learning was utilized to improve both the initial performance and learning efficiency. A hybrid loss function was employed to further optimize the segmentation model.

RESULTS

The model was tested on a data set of 616 ICAs obtained from 210 patients, composed of 437 images for training, 49 images for validation, and 130 images for testing. The segmentation model achieved a Dice score of 0.8942, a sensitivity of 0.8735, a specificity of 0.9954, and a Hausdorff distance of 6.0794 mm; it could predict arteries for a single ICA frame in 0.2114 seconds.

CONCLUSIONS

The results showed that our model outperformed the state-of-the-art deep-learning models. Our new method has great potential for clinical use.

摘要

背景

从有创性冠状动脉造影（ICA）图像中准确提取冠状动脉对于冠状动脉疾病（CAD）的诊断和风险分层至关重要。

目的

本研究提出了一种新的深度学习（DL）方法，用于从 ICA 图像中自动提取冠状动脉。

方法

开发了一个具有全尺度跳跃连接和全尺度深度监督的卷积神经网络（CNN）。编码器架构基于残差和 inception 模块，从具有不同窗口形状的多个卷积层中获取多尺度特征。迁移学习用于提高初始性能和学习效率。采用混合损失函数进一步优化分割模型。

结果

该模型在来自 210 名患者的 616 张 ICA 图像数据集上进行了测试，该数据集由 437 张用于训练的图像、49 张用于验证的图像和 130 张用于测试的图像组成。分割模型的 Dice 评分达到 0.8942，灵敏度为 0.8735，特异性为 0.9954，Hausdorff 距离为 6.0794mm；它可以在 0.2114 秒内预测单个 ICA 帧的动脉。