Structure-Consistent Restoration Network for Cataract Fundus Image Enhancement

Li，Heng1; Liu，Haofeng1; Fu，Huazhu2; Shu，Hai3; Zhao，Yitian4; Luo，Xiaoling5; Hu，Yan1; Liu，Jiang1,6,7

10.1007/978-3-031-16434-7_47

2022

25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

0302-9743

1611-3349

978-3-031-16433-0

LECT NOTES ARTIF INT

13432 LNCS

487-496

SEP 18-22, 2022

null,Singapore,SINGAPORE

GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

SPRINGER INTERNATIONAL PUBLISHING AG

Fundus photography is a routine examination in clinics to diagnose and monitor ocular diseases. However, for cataract patients, the fundus image always suffers quality degradation caused by the clouding lens. The degradation prevents reliable diagnosis by ophthalmologists or computer-aided systems. To improve the certainty in clinical diagnosis, restoration algorithms have been proposed to enhance the quality of fundus images. Unfortunately, challenges remain in the deployment of these algorithms, such as collecting sufficient training data and preserving retinal structures. In this paper, to circumvent the strict deployment requirement, a structure-consistent restoration network (SCR-Net) for cataract fundus images is developed from synthesized data that shares an identical structure. A cataract simulation model is firstly designed to collect synthesized cataract sets (SCS) formed by cataract fundus images sharing identical structures. Then high-frequency components (HFCs) are extracted from the SCS to constrain structure consistency such that the structure preservation in SCR-Net is enforced. The experiments demonstrate the effectiveness of SCR-Net in the comparison with state-of-the-art methods and the follow-up clinical applications. The code is available at https://github.com/liamheng/Annotation-free-Fundus-Image-Enhancement.

Cataract Fundus image enhancement High-frequency components Structure consistency

First ; Corresponding

English

CPCI-S

Basic and Applied Fundamental Research Foundation of Guangdong Province[2020A1515110286] ; National Natural Science Foundation of China[8210072776] ; Guangdong Provincial Department of Education[2020ZDZX3043] ; Guangdong Provincial Key Laboratory[2020B121201001] ; Shenzhen Natural Science Fund["JCYJ20200109140820699","20200925174052004"] ; A*STAR AME Programmatic Fund[A20H4b0141]

Computer Science ; Radiology, Nuclear Medicine & Medical Imaging

Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Radiology, Nuclear Medicine & Medical Imaging

WOS:000867288800047

2-s2.0-85138999261

Scopus

1.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China
2.IHPC,A*STAR,Singapore,Singapore
3.Department of Biostatistics,School of Global Public Health,New York University,New York,United States
4.Cixi Institute of Biomedical Engineering,Chinese Academy of Sciences,Beijing,China
5.Shenzhen People’s Hospital,Shenzhen,China
6.Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation,Southern University of Science and Technology,Shenzhen,China
7.Research Institute of Trustworthy Autonomous Systems,Southern University of Science and Technology,Shenzhen,China

Li，Heng,Liu，Haofeng,Fu，Huazhu,et al. Structure-Consistent Restoration Network for Cataract Fundus Image Enhancement[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2022:487-496.