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

Wang，Mini Han1,2,3,4,5; Huang，Lina6; Hou，Guanghui7; Yang，Jie8; Xing，Lumin9; Yuan，Qiting10,11; Chong，Kelvin Kam Lung1; Lin，Zhiyuan5,12; Zeng，Peijin5,12; Fang，Xiaoxiao5,7; Yao，Xiaoping13; Li，Qingqian14; Liu，Jiang15; Lin，Chen16

10.1117/12.3017895

2024

0277-786X

1996-756X

Proceedings of SPIE - The International Society for Optical Engineering

12983

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.

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

其他

英语

EI

2-s2.0-85184281562

Scopus

1.Department of Ophthalmology & Visual Sciences,Faculty of Medicine,Chinese University of Hong Kong,Hong Kong,999077,Hong Kong
2.The faculty of Data Science,City University of Macau,999078,Macao
3.Zhuhai People's Hospital,Zhuhai Hospital Affiliated with Jinan University,Zhuhai,519000,China
4.The Department of AI and big data application,Zhuhai Institute of Advanced Technology Chinese Academy of Sciences,Zhuhai,519000,China
5.Perspective Technology Group,Zhuhai,519000,China
6.Jinan University Affiliated Shenzhen Eye Hospital,Shenzhen,518000,China
7.Aier Eye Hospital of Zhuhai,Zhuhai,519000,China
8.College of Artificial Intelligence,Chongqing Industry & Trade Polytechnic,Chongqing,408000,China
9.The First Affiliated Hospital,Shandong First Medical University,Shandong Provincial Qianfoshan Hospital,Jinan,250014,China
10.Business School,Hong Kong University of Science and Technology,Hong Kong,999077,Hong Kong
11.Zhimou Medical (Shenzhen) Company Ltd.,Shenzhen,Guangdong,518000,China
12.School of Computer,Beijing Institute of Technology Zhuhai,Zhuhai,519000,China
13.Union Hospital of Fujian Medical University,Fuzhou,353000,China
14.Department of Ophthalmology,People's Hospital of Yantian District,Shenzhen,518000,China
15.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518000,China
16.Department of Ophthalmology,Shenzhen People's Hospital,Shenzhen,518000,China

Wang，Mini Han,Huang，Lina,Hou，Guanghui,et al. Deep learning for macular fovea detection based on ultra-widefield fundus images[C],2024.