@inproceedings{08056cafea924a1aa85726c519aa895f,
title = "Deep learning for macular fovea detection based on ultra-widefield fundus images",
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.",
keywords = "deep learning, fully convolutional networks, macula fovea detection, U-shape network, ultra-widefield Fundus images",
author = "Wang, {Mini Han} and Lina Huang and Guanghui Hou and Jie Yang and Lumin Xing and Qiting Yuan and Chong, {Kelvin Kam Lung} and Zhiyuan Lin and Peijin Zeng and Xiaoxiao Fang and Xiaoping Yao and Qingqian Li and Jiang Liu and Chen Lin",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; 2nd International Conference on Electrical, Electronics, and Information Engineering, EEIE 2023 ; Conference date: 02-11-2023 Through 04-11-2023",
year = "2024",
doi = "10.1117/12.3017895",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Tania Limongi and Hong Lin",
booktitle = "Second International Conference on Electrical, Electronics, and Information Engineering, EEIE 2023",
address = "United States",
}