深度学习辅助早产儿视网膜病变(ROP)筛查

Vijay Kumar, Het Patel, Kolin Paul, S. Azad
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引用次数: 0

摘要

早产儿视网膜病变(ROP)是全世界早产儿失明的主要原因,特别是在发展中国家。在这项研究中,我们提出了一种基于深度卷积神经网络(DCNN)和图像处理的方法来自动检测视网膜特征,包括光盘(OD)和视网膜血管(BV),并使用基于规则的方法对ROP患者进行疾病分类。我们的DCNN模型使用YOLO-v5进行OD检测,使用Pix2Pix或U-Net进行BV分割。我们在公开的眼底图像数据集(尺寸为1117和288)上训练DCNN模型,分别用于OD检测和BV分割。我们在439张早产儿视网膜图像的数据集上评估了我们的方法,测试了ROP区和6个BV面罩。我们提出的系统取得了优异的效果,OD检测模块的总体准确率为98.94% (IoU为0.5时),BV分割模块的总体准确率为96.69%,Dice系数在0.60 ~ 0.64之间。此外,该系统可准确诊断1区ROP,准确率为88.23%。我们的方法为准确的ROP筛查和诊断提供了一个有希望的解决方案,特别是在资源匮乏的环境中,它有可能改善医疗保健结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning-assisted Retinopathy of Prematurity (ROP) Screening
Retinopathy of prematurity (ROP) is a leading cause of blindness in premature infants worldwide, particularly in developing countries. In this research, we propose a Deep Convolutional Neural Network (DCNN) and image processing-based approach for the automatic detection of retinal features, including the optical disc (OD) and retinal blood vessels (BV), as well as disease classification using a rule-based method for ROP patients. Our DCNN model uses YOLO-v5 for OD detection and either Pix2Pix or a U-Net for BV segmentation. We trained our DCNN models on publicly available fundus image datasets of size 1,117 and 288 for OD detection and BV segmentation, respectively. We evaluated our approach on a dataset of 439 preterm neonatal retinal images, testing for ROP Zone and 6 BV masks. Our proposed system achieved excellent results, with the OD detection module achieving an overall accuracy of 98.94% (when IoU 0.5) and the BV segmentation module achieving an accuracy of 96.69% and a Dice coefficient between 0.60 and 0.64. Moreover, our system accurately diagnosed ROP in Zone-1 with 88.23% accuracy. Our approach offers a promising solution for accurate ROP screening and diagnosis, particularly in low-resource settings, where it has the potential to improve healthcare outcomes.
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CiteScore
10.30
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