糖尿病视网膜病变的智能筛查

戴玉兰; 朱承璋; 单希; 程真真; 邹北骥

doi:10.24920/003587

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糖尿病视网膜病变的智能筛查

中国医学科学杂志（英文版） 2019年34卷第2期页码：120-132

Review | Updated：2024-04-10

- 糖尿病视网膜病变的智能筛查
- Affiliations：
  
  1. School of Computing Science and Engineering
  2. School of Literature and Journalism, Central South University, Changsha 410083, China
  3. Hunan Province Engineering Technology Research Center of Computer Vision and Intelligent Medical Treatment, Changsha 410083, China
- Author bio：
  
  *, E-mail: anandawork@126.com
- Funds：
  
  中南大学中央高校基本科研业务费专项基金(No. 2019zzts591)；湖南省自然科学基金（No. 2018JJ3686）。Supported by the Natural Science Foundation of Hunan Province (No. 2018JJ3686) and the Fundamental Research Fund for the Central Universities of Central South University (No. 2019zzts591).
- DOI：10.24920/003587
  中图分类号：
- 收稿日期：2019-03-22，
  
  录用日期：2019-5-6，
  
  网络出版日期：2019-05-20，
  
  纸质出版日期：2019-05-20
- Accepted：
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戴玉兰, 朱承璋, 单希, 等. 糖尿病视网膜病变的智能筛查[J]. 中国医学科学杂志（英文版）, 2019,34(2):120-132.

Dai Yulan, Zhu Chengzhang, Shan Xi, et al. A Survey on Intelligent Screening for Diabetic Retinopathy[J]. Chinese medical sciences journal;chinese medical sciences journal, 2019, 34(2): 120-132.
戴玉兰, 朱承璋, 单希, 等. 糖尿病视网膜病变的智能筛查[J]. 中国医学科学杂志（英文版）, 2019,34(2):120-132. DOI： 10.24920/003587.

Dai Yulan, Zhu Chengzhang, Shan Xi, et al. A Survey on Intelligent Screening for Diabetic Retinopathy[J]. Chinese medical sciences journal;chinese medical sciences journal, 2019, 34(2): 120-132. DOI： 10.24920/003587.

摘要

糖尿病视网膜病变是导致失明的主要原因之一

但通过筛查、早期诊断和治疗可以有效避免该并发症的发生。为了提高糖尿病视网膜病变筛查的广泛性和效率

研究者们在智能筛查方面投入了大量的精力

并取得了较大的进展。本文从四个方面对糖尿病视网膜病变的筛查进行了系统地综述: 1) 公共彩色眼底图像数据集;2) 分类及相关病变提取方法;3)现有的计算机辅助筛查系统;4)存在的问题、挑战和研究趋势。希望此文能为糖尿病视网膜病变智能筛查的未来研究方向提供参考。

Abstract

Diabetic retinopathy (DR) is one of the leading causes of vision loss and can be effectively avoided by screening

early diagnosis and treatment. In order to increase the universality and efficiency of DR screening

many efforts have been invested in developing intelligent screening

and there have been great advances. In this paper

we survey DR screening from four perspectives: 1) public color fundus image datasets of DR; 2) DR classification and related lesion-extraction approaches; 3) existing computer-aided systems for DR screening; and 4) existing issues

challenges

and research trends. Our goal is to provide insights for future research directions on DR intelligent screening.

关键词

Keywords

references

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