Retinal OCT image segmentation with deep learning: A review of advances, datasets, and evaluation metrics

IF 5.4 2区医学 Q1 ENGINEERING, BIOMEDICAL

Computerized Medical Imaging and Graphics Pub Date : 2025-04-04 DOI:10.1016/j.compmedimag.2025.102539

Huihong Zhang , Bing Yang , Sanqian Li , Xiaoqing Zhang , Xiaoling Li , Tianhang Liu , Risa Higashita , Jiang Liu

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引用次数: 0

Abstract

Optical coherence tomography (OCT) is a widely used imaging technology in ophthalmic clinical practice, providing non-invasive access to high-resolution retinal images. Segmentation of anatomical structures and pathological lesions in retinal OCT images, directly impacts clinical decisions. While commercial OCT devices segment multiple retinal layers in healthy eyes, their performance degrades severely under pathological conditions. In recent years, the rapid advancements in deep learning have significantly driven research in OCT image segmentation. This review provides a comprehensive overview of the latest developments in deep learning-based segmentation methods for retinal OCT images. Additionally, it summarizes the medical significance, publicly available datasets, and commonly used evaluation metrics in this field. The review also discusses the current challenges faced by the research community and highlights potential future directions.

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视网膜OCT图像分割与深度学习：回顾进展，数据集，和评估指标

光学相干断层扫描（OCT）是眼科临床实践中广泛使用的成像技术，可提供无创的高分辨率视网膜图像。视网膜 OCT 图像中解剖结构和病变的分割直接影响临床决策。虽然商用 OCT 设备能对健康眼睛的多个视网膜层进行分割，但在病理条件下，其性能会严重下降。近年来，深度学习的快速发展极大地推动了 OCT 图像分割的研究。本综述全面概述了基于深度学习的视网膜 OCT 图像分割方法的最新进展。此外，它还总结了该领域的医学意义、公开可用的数据集和常用的评估指标。该综述还讨论了研究界目前面临的挑战，并强调了未来的潜在发展方向。

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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.