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Unpaired fundus image enhancement based on constrained generative adversarial networks
  • +12
  • Luyao Yang,
  • Shenglan Yao,
  • Pengyu Chen,
  • Mei Shen,
  • Suzhong Fu,
  • Jiwei Xing,
  • Yunxin Xue,
  • Xin Chen,
  • Xiaofei Wen,
  • Yang Zhao,
  • Wei Li,
  • Heng Ma,
  • Shiying Li,
  • Valery Tuchin,
  • Qingliang Zhao
Luyao Yang
Center for Molecular Imaging and Translational Medicine School of Public Health Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM) Xiamen University
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Shenglan Yao
Center for Molecular Imaging and Translational Medicine School of Public Health Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM) Xiamen University
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Pengyu Chen
Center for Molecular Imaging and Translational Medicine School of Public Health Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM) Xiamen University
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Mei Shen
Xiang'an Hospital of Xiamen University
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Suzhong Fu
Center for Molecular Imaging and Translational Medicine School of Public Health Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM) Xiamen University
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Jiwei Xing
Center for Molecular Imaging and Translational Medicine School of Public Health Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM) Xiamen University
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Yunxin Xue
Center for Molecular Imaging and Translational Medicine School of Public Health Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM) Xiamen University
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Xin Chen
Department of Orthopedics and Traumatology of Traditional Chinese Medicine Xiamen Third Hospital
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Xiaofei Wen
Affiliated Hospital of Xiamen University
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Yang Zhao
Xiamen University
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Wei Li
Xiang'an Hospital of Xiamen University
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Heng Ma
Fourth Military Medical University
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Shiying Li
Xiang'an Hospital of Xiamen University
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Valery Tuchin
Saratov State University
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Qingliang Zhao
Center for Molecular Imaging and Translational Medicine School of Public Health Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM) Xiamen University

Corresponding Author:zhaoql@xmu.edu.cn

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Abstract

Fundus photography (FP) is a crucial technique for diagnosing the progression of ocular and systemic diseases in clinical studies, with wide applications in early clinical screening and diagnosis. However, due to the non-uniform illumination and imbalanced intensity caused by various reasons, the quality of fundus images is often severely weakened, brings challenges for automated screening, analysis and diagnosis of diseases. To resolve this problem, we developed strongly constrained generative adversarial networks (SCGAN). The results demonstrate that the quality of various datasets were more significantly enhanced based on SCGAN, simultaneously more effectively retaining tissue and vascular information under various experimental conditions . Furthermore, the clinical effectiveness and robustness of this model was validated by showing its improved ability in vascular segmentation as well as disease diagnosis. Our study provides a new comprehensive approach for FP and also possesses the potential capacity to advance artificial intelligence-assisted ophthalmic examination.
21 Apr 2024Submitted to Journal of Biophotonics
21 Apr 2024Submission Checks Completed
21 Apr 2024Assigned to Editor
21 Apr 2024Review(s) Completed, Editorial Evaluation Pending
10 May 20241st Revision Received
29 May 2024Editorial Decision: Accept