非增殖性糖尿病视网膜病变数据集(NDRD):用于糖尿病视网膜病变筛查研究和深度学习评估的数据库。

IF 2.2 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Xing Liang, Haiqi Wen, Yajian Duan, Kan He, Xiufang Feng, Guohong Zhou
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引用次数: 0

摘要

目的本文提供了一个非增殖性糖尿病视网膜病变数据库,重点关注早期糖尿病视网膜病变的硬性渗出,并进一步探讨其在疾病识别中的临床应用:方法:收集 Optos Panoramic 200 激光扫描眼底镜拍摄的非增殖性糖尿病视网膜病变照片,过滤掉质量较差的图片,并在专业医务人员的指导下对图片中的硬性渗出病变进行标注。为了验证数据集的有效性,我们使用了五个深度学习模型对数据集进行学习预测。此外,我们还利用评价指标对模型的性能进行了评估:非增殖性糖尿病视网膜病变比增殖性视网膜病变更小,更难识别。现有分割模型的病变分割性能较差,而针对小病变的深度病变分割模型的交集大于联合(IOU)值能达到66.12%,高于普通病变分割模型,但仍有很大的提升空间:结论:小型硬性渗出病灶的分割比大型硬性渗出病灶的分割更具挑战性。结论:与大面积硬性渗出病变相比,小面积硬性渗出病变的分割更具挑战性,需要更多有针对性的数据集进行模型训练。与之前的糖尿病视网膜数据集相比,NDRD 数据集更关注微小病变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Nonproliferative diabetic retinopathy dataset(NDRD): A database for diabetic retinopathy screening research and deep learning evaluation.

Objectives: In this article, we provide a database of nonproliferative diabetes retinopathy, which focuses on early diabetes retinopathy with hard exudation, and further explore its clinical application in disease recognition.

Methods: We collect the photos of nonproliferative diabetes retinopathy taken by Optos Panoramic 200 laser scanning ophthalmoscope, filter out the pictures with poor quality, and label the hard exudative lesions in the images under the guidance of professional medical personnel. To validate the effectiveness of the datasets, five deep learning models are used to perform learning predictions on the datasets. Furthermore, we evaluate the performance of the model using evaluation metrics.

Results: Nonproliferative diabetes retinopathy is smaller than proliferative retinopathy and more difficult to identify. The existing segmentation models have poor lesion segmentation performance, while the intersection over union (IOU) value for deep lesion segmentation of models targeting small lesions can reach 66.12%, which is higher than ordinary lesion segmentation models, but there is still a lot of room for improvement.

Conclusion: The segmentation of small hard exudative lesions is more challenging than that of large hard exudative lesions. More targeted datasets are needed for model training. Compared with the previous diabetes retina datasets, the NDRD dataset pays more attention to micro lesions.

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来源期刊
Health Informatics Journal
Health Informatics Journal HEALTH CARE SCIENCES & SERVICES-MEDICAL INFORMATICS
CiteScore
7.80
自引率
6.70%
发文量
80
审稿时长
6 months
期刊介绍: Health Informatics Journal is an international peer-reviewed journal. All papers submitted to Health Informatics Journal are subject to peer review by members of a carefully appointed editorial board. The journal operates a conventional single-blind reviewing policy in which the reviewer’s name is always concealed from the submitting author.
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