使用计算机视觉和深度学习算法在光学相干断层扫描（OCT）上自动检测玻璃体后脱离

Automated Detection of Posterior Vitreous Detachment on OCT Using Computer Vision and Deep Learning Algorithms.

作者信息

Li Alexa L, Feng Moira, Wang Zixi, Baxter Sally L, Huang Lingling, Arnett Justin, Bartsch Dirk-Uwe G, Kuo David E, Saseendrakumar Bharanidharan Radha, Guo Joy, Nudleman Eric

机构信息

Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California at San Diego, La Jolla, California.

Jacobs School of Engineering, University of California San Diego, La Jolla, California.

出版信息

Ophthalmol Sci. 2022 Nov 11;3(2):100254. doi: 10.1016/j.xops.2022.100254. eCollection 2023 Jun.

DOI:10.1016/j.xops.2022.100254

PMID:36691594

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9860346/

Abstract

OBJECTIVE

To develop automated algorithms for the detection of posterior vitreous detachment (PVD) using OCT imaging.

DESIGN

Evaluation of a diagnostic test or technology.

SUBJECTS

Overall, 42 385 consecutive OCT images (865 volumetric OCT scans) obtained with Heidelberg Spectralis from 865 eyes from 464 patients at an academic retina clinic between October 2020 and December 2021 were retrospectively reviewed.

METHODS

We developed a customized computer vision algorithm based on image filtering and edge detection to detect the posterior vitreous cortex for the determination of PVD status. A second deep learning (DL) image classification model based on convolutional neural networks and ResNet-50 architecture was also trained to identify PVD status from OCT images. The training dataset consisted of 674 OCT volume scans (33 026 OCT images), while the validation testing set consisted of 73 OCT volume scans (3577 OCT images). Overall, 118 OCT volume scans (5782 OCT images) were used as a separate external testing dataset.

MAIN OUTCOME MEASURES

Accuracy, sensitivity, specificity, F1-scores, and area under the receiver operator characteristic curves (AUROCs) were measured to assess the performance of the automated algorithms.

RESULTS

Both the customized computer vision algorithm and DL model results were largely in agreement with the PVD status labeled by trained graders. The DL approach achieved an accuracy of 90.7% and an F1-score of 0.932 with a sensitivity of 100% and a specificity of 74.5% for PVD detection from an OCT volume scan. The AUROC was 89% at the image level and 96% at the volume level for the DL model. The customized computer vision algorithm attained an accuracy of 89.5% and an F1-score of 0.912 with a sensitivity of 91.9% and a specificity of 86.1% on the same task.

CONCLUSIONS

Both the computer vision algorithm and the DL model applied on OCT imaging enabled reliable detection of PVD status, demonstrating the potential for OCT-based automated PVD status classification to assist with vitreoretinal surgical planning.

FINANCIAL DISCLOSURES

Proprietary or commercial disclosure may be found after the references.

摘要

目的

利用光学相干断层扫描（OCT）成像技术开发用于检测玻璃体后脱离（PVD）的自动化算法。

设计

对一种诊断测试或技术进行评估。

研究对象

回顾性分析了2020年10月至2021年12月期间在一家学术性视网膜诊所从464例患者的865只眼中，使用海德堡谱域OCT获取的42385幅连续OCT图像（865次容积OCT扫描）。

方法

我们开发了一种基于图像滤波和边缘检测的定制计算机视觉算法，用于检测玻璃体后皮质以确定PVD状态。还训练了一种基于卷积神经网络和ResNet-50架构的深度学习（DL）图像分类模型，以从OCT图像中识别PVD状态。训练数据集由674次容积OCT扫描（33026幅OCT图像）组成，而验证测试集由73次容积OCT扫描（3577幅OCT图像）组成。总体而言，118次容积OCT扫描（5782幅OCT图像）被用作单独的外部测试数据集。

主要观察指标

测量准确性、敏感性、特异性、F1分数和受试者操作特征曲线下面积（AUROC），以评估自动化算法的性能。

结果

定制的计算机视觉算法和DL模型的结果在很大程度上与训练有素的分级人员标记的PVD状态一致。DL方法从容积OCT扫描中检测PVD时，准确率达到90.7%，F1分数为0.932，敏感性为100%，特异性为74.5%。DL模型在图像层面的AUROC为89%，在容积层面为96%。定制的计算机视觉算法在同一任务上的准确率为89.5%，F1分数为0.912，敏感性为91.9%，特异性为86.1%。