Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

A Fast Lightweight Based Deep Fusion Learning for Detecting Macula Fovea Using Ultra-Widefield Fundus Images

Han Wang
* ,
Jie Yang
* ,
Yaoyang Wu
* ,
Wencai Du
* ,
Simon Fong
ORCID logo ,
Yutong Duan
,
Xiaoping Yao
,
Xiaoshu Zhou
,
Qingqian Li
,
Chen Lin
* ,
Jiang Liu
,
Lina Huang
* ,
Feng Wu
Version 1 : Received: 20 August 2021 / Approved: 24 August 2021 / Online: 24 August 2021 (13:57:56 CEST)
Version 2 : Received: 6 September 2021 / Approved: 7 September 2021 / Online: 7 September 2021 (11:51:17 CEST)

A peer-reviewed article of this Preprint also exists.

Wang, M. H., Huang, L., Hou, G., Yang, J., Xing, L., Yuan, Q., ... & Lin, C. (2024, January). Deep learning for macular fovea detection based on ultra-widefield fundus images. In Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023) (Vol. 12983, pp. 510-521). SPIE. Wang, M. H., Huang, L., Hou, G., Yang, J., Xing, L., Yuan, Q., ... & Lin, C. (2024, January). Deep learning for macular fovea detection based on ultra-widefield fundus images. In Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023) (Vol. 12983, pp. 510-521). SPIE.

Abstract

Macula fovea detection is a crucial prerequisite towards 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 accuracy nor effectiveness of the diagnose process could be guaranteed. In this project, we proposed a deep learning approach on ultra-widefield fundus (UWF) images for macula fovea detection. This study collected 2300 ultra-widefield fundus images from Shenzhen Aier Eye Hospital in China. Methods based on U-shape network (Unet) and Fully Convolutional Networks (FCN) are 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 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. Results are measured by calculating the Euclidean distance between proposed approaches and the accurate grounded standard, which is detected by Ultra-widefield swept-source optical coherence tomograph (UWF-OCT) approach. Through a comparation of proposed methods, we conclude that, deep learning approach of Unet outperformed other methods on macula fovea detection tasks, by which outcomes obtained are comparable to grounded standard method.

Keywords

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

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Comments (1)

Comment 1
Received: 7 September 2021
Commenter: Han Wang
Commenter's Conflict of Interests: Author
Comment: The discussion and visualization are revised.
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