Diagnosing Diabetic Retinopathy in OCTA Images Based on Multilevel Information Fusion Using a Deep Learning Framework.

Journal: Computational and mathematical methods in medicine
Published Date: Aug 4, 2022

Abstract

OBJECTIVE: As an extension of optical coherence tomography (OCT), optical coherence tomographic angiography (OCTA) provides information on the blood flow status at the microlevel and is sensitive to changes in the fundus vessels. However, due to the distinct imaging mechanism of OCTA, existing models, which are primarily used for analyzing fundus images, do not work well on OCTA images. Effectively extracting and analyzing the information in OCTA images remains challenging. To this end, a deep learning framework that fuses multilevel information in OCTA images is proposed in this study. The effectiveness of the proposed model was demonstrated in the task of diabetic retinopathy (DR) classification.

Authors

  • Qiaoyu Li
    Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
  • Xiao-Rong Zhu
    Department of Endocrinology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China.
  • Guangmin Sun
    Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
  • Lin Zhang
    Laboratory of Molecular Translational Medicine, Centre for Translational Medicine, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Clinical Research Center for Birth Defects of Sichuan Province, West China Second Hospital, Sichuan University, Chengdu, Sichuan, 610041, China. Electronic address: zhanglin@scu.edu.cn.
  • Meilong Zhu
    Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
  • Tian Tian
    Laboratory Animal Center College of Animal Science Jilin University Changchun China.
  • Chenyu Guo
    Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
  • Sarah Mazhar
    Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
  • Jin-Kui Yang
    Department of Endocrinology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China.
  • Yu Li
    Department of Public Health, Shihezi University School of Medicine, 832000, China.

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