Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed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

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)

How to cite: Wang, H.; Yang, J.; Wu, Y.; Du, W.; Fong, S.; Duan, Y.; Yao, X.; Zhou, X.; Li, Q.; Lin, C.; Liu, J.; Huang, L.; Wu, F. A Fast Lightweight Based Deep Fusion Learning for Detecting Macula Fovea Using Ultra-Widefield Fundus Images. Preprints 2021, 2021080469 (doi: 10.20944/preprints202108.0469.v2). Wang, H.; Yang, J.; Wu, Y.; Du, W.; Fong, S.; Duan, Y.; Yao, X.; Zhou, X.; Li, Q.; Lin, C.; Liu, J.; Huang, L.; Wu, F. A Fast Lightweight Based Deep Fusion Learning for Detecting Macula Fovea Using Ultra-Widefield Fundus Images. Preprints 2021, 2021080469 (doi: 10.20944/preprints202108.0469.v2).

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

MATHEMATICS & COMPUTER SCIENCE, Other

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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