Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning

返回OCT图像分类​​​​​​​

使用深度学习OCT中黄斑液全自动检测和定量分析

期刊Ophthalmology（眼科学）

刊名：Ophthalmology

国家：美国

周期：月刊

影响因子：5.454

网址：http://scienced...l/01616420

简介： 《眼科学》是美国眼科学会（American Academy of Ophthalmology）的官方杂志，刊载有关眼科的原创性、同行评议性研究报告，包括基础与临床研究。报道内容包括新的诊断和外科技术、治疗方法、器械更新、最新药物研究发现、临床试验结果和研究发现等。《眼科学》还发表由公认的权威机构就特殊课题撰写的重大综述材料。美国眼科学会推出《眼科学》，旨在为观念和信息的自由交流提供机会。

http://paper.medlive.cn/jour/18597

论文地址

http://paper.medlive.cn/jour/18597

摘要：

Purpose: Development and validation of a fully automated method to detect and quantify macular fluid in
conventional OCT images.
Design: Development of a diagnostic modality.
Participants: The clinical dataset for fluid detection consisted of 1200 OCT volumes of patients with neo-
vascular age-related macular degeneration (AMD, n ¼ 400), diabetic macular edema (DME, n ¼ 400), or retinal
vein occlusion (RVO, n ¼ 400) acquired with Zeiss Cirrus (Carl Zeiss Meditec, Dublin, CA) (n ¼ 600) or Heidelberg
Spectralis (Heidelberg Engineering, Heidelberg, Germany) (n ¼ 600) OCT devices.
Methods: A method based on deep learning to automatically detect and quantify intraretinal cystoid fluid
(IRC) and subretinal fluid (SRF) was developed. The performance of the algorithm in accurately identifying fluid
localization and extent was evaluated against a manual consensus reading of 2 masked reading center graders.
Main Outcome Measures: Performance of a fully automated method to accurately detect, differentiate, and
quantify intraretinal and SRF using area under the receiver operating characteristics curves, precision, and recall.
Results: The newly designed, fully automated diagnostic method based on deep learning achieved optimal
accuracy for the detection and quantification of IRC for all 3 macular pathologies with a mean accuracy (AUC) of 0.94
(range, 0.91e0.97), a mean precision of 0.91, and a mean recall of 0.84. The detection and measurement of SRF were
also highly accurate with an AUC of 0.92 (range, 0.86e0.98), a mean precision of 0.61, and a mean recall of 0.81, with
superior performance in neovascular AMD and RVO compared with DME, which was represented rarely in the
population studied. High linear correlation was confirmed between automated and manual fluid localization and
quantification, yielding an average Pearson’s correlation coefficient of 0.90 for IRC and of 0.96 for SRF.
Conclusions: Deep learning in retinal image analysis achieves excellent accuracy for the differential detec-
tion of retinal fluid types across the most prevalent exudative macular diseases and OCT devices. Furthermore,
quantification of fluid achieves a high level of concordance with manual expert assessment

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