Deep learning based joint segmentation and characterization of multi-class retinal fluid lesions on OCT scans for clinical use in anti-VEGF therapy.

Journal: Computers in biology and medicine
Published Date: Aug 4, 2021

Abstract

BACKGROUND: In anti-vascular endothelial growth factor (anti-VEGF) therapy, an accurate estimation of multi-class retinal fluid (MRF) is required for the activity prescription and intravitreal dose. This study proposes an end-to-end deep learning-based retinal fluids segmentation network (RFS-Net) to segment and recognize three MRF lesion manifestations, namely, intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED), from multi-vendor optical coherence tomography (OCT) imagery. The proposed image analysis tool will optimize anti-VEGF therapy and contribute to reducing the inter- and intra-observer variability.

Authors

  • Bilal Hassan
    School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, 100191, China. Electronic address: bilalhassan@buaa.edu.cn.
  • Shiyin Qin
    School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, 100191, China; School of Electrical Engineering and Intelligentization, Dongguan University of Technology, Dongguan, 523808, China.
  • Ramsha Ahmed
    School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing, 100083, China.
  • Taimur Hassan
    Department of Computer Engineering, National University of Sciences and Technology, Islamabad, Pakistan.
  • Abdel Hakeem Taguri
    Abu Dhabi Healthcare Company (SEHA), Abu Dhabi, 127788, United Arab Emirates.
  • Shahrukh Hashmi
  • Naoufel Werghi
    Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, United Arab Emirates.

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