基于深度学习的OCT/OCTA视网膜图像分析方法综述

doi:10.11896/jsjkx.241100047

摘要/Abstract

摘要： 深度学习是人工智能的一个分支,其依赖深度神经网络进行数据处理与分析。近年来,深度学习在医学影像领域,尤其在图像分类、分割及疗效评估方面取得了显著突破。在眼科领域,应用深度学习技术高效、准确分析光学相干断层扫描成像(Optical Coherence Tomography,OCT)和光学相干断层扫描血管成像(Optical Coherence Tomography Angiography,OCTA)的需求日益增加。相比传统的手工方法,深度学习方法在处理复杂眼底结构和病理变化时展现出更高的精度和更强的自动化能力。然而,以往的综述多侧重于单一成像模式或单一任务的研究,往往忽视了不同成像技术之间的相关性以及任务间的承接性和关联性。对此,不仅详细总结了常用数据集,系统回顾了基于不同OCT和OCTA设备的视网膜相关疾病生物标志物的分割方法,还从不同疾病特性的角度总结了视网膜相关疾病的典型分类方法。最后,从数据隐私与安全性、模型可解释性,以及模型通用性等角度展望了未来的研究方向,为后续研究提供了有价值的参考。

关键词: OCT/OCTA, 深度学习, 生物标志物, 图像分割, 疾病分类

Abstract: Deep learning is a branch of artificial intelligence that relies on deep neural networks for data processing and analysis.In recent years,deep learning has made significant breakthroughs in the field of medical imaging,especially in image classification,segmentation and efficacy evaluation.In the field of ophthalmology,there is an increasing need to apply deep learning techniques for efficient and accurate analysis of OCT and OCTA.Compared with traditional manual methods,deep learning methods show higher accuracy and automation in dealing with complex fundus structure and pathological changes.However,most of the previous reviews focuse on single imaging mode or single task research,and often ignore the correlation between different imaging technology,as well as the acceptability and correlation between tasks.This paper not only summarizes the commonly used data sets,systematically reviews the segmentation methods of retina-related disease biomarkers based on different OCT and OCTA devices,but also summarizes the typical classification methods of retina-related diseases from the perspective of different disease characteristics.Finally,this paper also looks forward to the future research direction from the perspectives of data privacy and security,model interpretability,and model universality,which provides a valuable reference for subsequent research.

Key words: OCT/OCTA, Deep learning, Biomarkers, Image segmentation, Classification of diseases

中图分类号:

TP391

薛静艳, 夏佳楠, 霍蕊莉, 刘杰, 周雪忠. 基于深度学习的OCT/OCTA视网膜图像分析方法综述[J]. 计算机科学, 2026, 53(1): 128-140. https://doi.org/10.11896/jsjkx.241100047

XUE Jingyan, XIA Jianan, HUO Ruili, LIU Jie, ZHOU Xuezhong. Review of Retinal Image Analysis Methods for OCT/OCTA Based on Deep Learning[J]. Computer Science, 2026, 53(1): 128-140. https://doi.org/10.11896/jsjkx.241100047

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