Deep learning for macular fovea detection based on ultra-widefield fundus images

Mini Han Wang, Lina Huang, Guanghui Hou, Jie Yang, Lumin Xing, Qiting Yuan, Kelvin Kam Lung Chong, Zhiyuan Lin, Peijin Zeng, Xiaoxiao Fang, Xiaoping Yao, Qingqian Li, Jiang Liu, Chen Lin

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

Macula fovea detection is a crucial molecular biological prerequisite for screening and diagnosing macular diseases. Without early detection and proper treatment, any abnormality involving the macula may lead to blindness. However, with the ophthalmologist shortage and time-consuming artificial evaluation, neither the accuracy nor effectiveness of the diagnosis process could be guaranteed. In this project, we proposed a light-weighted deep learning model based on ultra-widefield fundus (UWF) images for macula fovea detection tasks. This study collected 2300 ultra-widefield fundus images from Shenzhen Aier Eye Hospital in China. A light-weighted method based on a U-shape network (Unet) and Fully Convolution Network (FCN) approach is implemented on 1800 (before amplifying process) training fundus images, 400 (before amplifying process) validation images, and 100 test images. Three professional ophthalmologists were invited to mark the fovea. A method from the anatomy perspective is investigated. This approach is derived from the spatial relationship between the macula fovea and optic disc center in UWF. A set of parameters of this method is set based on the experience of ophthalmologists and verified to be effective. The ultra-widefield swept-source optical coherence tomography (UWF-OCT) approach is the grounded method. Through a comparison of proposed methods, we conclude that the proposed light-weighted Unet method outperformed other methods on macula fovea detection tasks.

Original languageEnglish
Title of host publicationSecond International Conference on Electrical, Electronics, and Information Engineering, EEIE 2023
EditorsTania Limongi, Hong Lin
PublisherSPIE
ISBN (Electronic)9781510672840
DOIs
Publication statusPublished - 2024
Externally publishedYes
Event2nd International Conference on Electrical, Electronics, and Information Engineering, EEIE 2023 - Wuhan, China
Duration: 2 Nov 20234 Nov 2023

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12983
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference2nd International Conference on Electrical, Electronics, and Information Engineering, EEIE 2023
Country/TerritoryChina
CityWuhan
Period2/11/234/11/23

Keywords

  • U-shape network
  • deep learning
  • fully convolutional networks
  • macula fovea detection
  • ultra-widefield Fundus images

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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