Automatic artery/vein classification methods for retinal blood vessel: A review

IF 5.4 2区医学 Q1 ENGINEERING, BIOMEDICAL Computerized Medical Imaging and Graphics Pub Date : 2024-02-16 DOI:10.1016/j.compmedimag.2024.102355

Qihan Chen , Jianqing Peng , Shen Zhao , Wanquan Liu

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Abstract

Automatic retinal arteriovenous classification can assist ophthalmologists in disease early diagnosis. Deep learning-based methods and topological graph-based methods have become the main solutions for retinal arteriovenous classification in recent years. This paper reviews the automatic retinal arteriovenous classification methods from 2003 to 2022. Firstly, we compare different methods and provide comparison tables of the summary results. Secondly, we complete the classification of the public arteriovenous classification datasets and provide the annotation development tables of different datasets. Finally, we sort out the challenges of evaluation methods and provide a comprehensive evaluation system. Quantitative and qualitative analysis shows the changes in research hotspots over time, Quantitative and qualitative analyses reveal the evolution of research hotspots over time, highlighting the significance of exploring the integration of deep learning with topological information in future research.

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视网膜血管的自动动脉/静脉分类方法：综述

视网膜动静脉自动分类可以帮助眼科医生进行疾病的早期诊断。近年来，基于深度学习的方法和基于拓扑图的方法已成为视网膜动静脉分类的主要解决方案。本文回顾了 2003 年至 2022 年的视网膜动静脉自动分类方法。首先，我们比较了不同的方法，并提供了汇总结果对比表。其次，我们完成了公共动静脉分类数据集的分类，并提供了不同数据集的注释开发表。最后，我们梳理了评价方法所面临的挑战，并提供了一个全面的评价体系。定量和定性分析显示了研究热点随时间的变化，定量和定性分析揭示了研究热点随时间的演变，凸显了在未来研究中探索深度学习与拓扑信息整合的意义。

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来源期刊

Computerized Medical Imaging and Graphics 医学-核医学

CiteScore

10.70

自引率

3.50%

发文量

审稿时长

26 days

期刊介绍： The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.