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15 January 2024 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
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Proceedings Volume 12983, Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023); 129831Z (2024) https://doi.org/10.1117/12.3017895
Event: Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023), 2023, Wuhan, China
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.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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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, and Chen Lin "Deep learning for macular fovea detection based on ultra-widefield fundus images", Proc. SPIE 12983, Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023), 129831Z (15 January 2024); https://doi.org/10.1117/12.3017895
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KEYWORDS
Deep learning
Anatomy
Macula
Error analysis
Eye
Image segmentation
Education and training
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