摘要
近年来,眼科作为高度依赖辅助成像的医学领域之一,一直处于深度学习算法应用的前沿。脉络膜的形态变化与眼底疾病的发生、发展以及治疗预后密切相关。光学相干断层扫描的快速发展极大地促进了对脉络膜形态和结构的精确分析。脉络膜分割及相关分析对于确定眼病的发病机制和治疗策略至关重要,然而,目前脉络膜主要依赖于繁琐、耗时和重复性低的人工手动分割。为了克服这些困难,近年来开发了用于脉络膜分割的深度学习方法,极大地提高了脉络膜分割的准确性和效率。本文旨在回顾不同眼病中脉络膜厚度的特征,探讨深度学习模型在测量脉络膜厚度中的最新应用及其优势,同时关注深度学习模型面临的挑战。
In recent years,ophthalmology,as one of the medical fields highly dependent on auxiliary imaging,has been at the forefront of the application of deep learning algorithm.The morphological changes of the choroid are closely related to the occurrence,development,treatment and prognosis of fundus diseases.The rapid development of optical coherence tomography has greatly promoted the accurate analysis of choroidal morphology and structure.Choroidal segmentation and related analysis are crucial for determining the pathogenesis and treatment strategies of eye diseases.However,currently,choroidal mainly relies on tedious,time-consuming,and low-reproducibility manual segmentation.To overcome these difficulties,deep learning methods for choroidal segmentation have been developed in recent years,greatly improving the accuracy and efficiency of choroidal segmentation.The purpose of this paper is to review the features of choroidal thickness in different eye diseases,explore the latest applications and advantages of deep learning models in measuring choroidal thickness,and focus on the challenges faced by deep learning models.
作者
周愉
张敏
朱瑜洁
陆琼
Yu Zhou;Min Zhang;Yu-Jie Zhu;Qiong Lu(Department of Ophthalmology,Luwan Branch of Ruijin Hospital,Shanghai Jiao Tong University School of Medicine,Shanghai 200020,China)
出处
《国际眼科杂志》
CAS
北大核心
2023年第6期1007-1011,共5页
International Eye Science
关键词
脉络膜厚度
脉络膜分割
深度学习
增强深度成像的光学相干断层扫描
卷积神经网络
choroidal thickness
choroidal segmentation
deep learning
enhanced depth imaging optical coherence tomography
convolutional neural networks
作者简介
周愉,毕业于同济大学,硕士,住院医师,研究方向:白内障、眼底病;通讯作者:陆琼,毕业于上海交通大学医学院,主任医师,研究方向:白内障、青光眼.luqiong99@sina.com。
