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• W3165736781 abstract "Age-related macular degeneration (AMD) is one of the leading causes of irreversible blindness and is characterized by fluid-related accumulations such as intra-retinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED). Spectral-domain optical coherence tomography (SD-OCT) is the primary modality used to diagnose AMD, yet it does not have algorithms that directly detect and quantify the fluid. This work presents an improved convolutional neural network (CNN)-based architecture called RetFluidNet to segment three types of fluid abnormalities from SD-OCT images. The model assimilates different skip-connect operations and atrous spatial pyramid pooling (ASPP) to integrate multi-scale contextual information; thus, achieving the best performance. This work also investigates between consequential and comparatively inconsequential hyperparameters and skip-connect techniques for fluid segmentation from the SD-OCT image to indicate the starting choice for future related researches. RetFluidNet was trained and tested on SD-OCT images from 124 patients and achieved an accuracy of 80.05%, 92.74%, and 95.53% for IRF, PED, and SRF, respectively. RetFluidNet showed significant improvement over competitive works to be clinically applicable in reasonable accuracy and time efficiency. RetFluidNet is a fully automated method that can support early detection and follow-up of AMD." @default.
• W3165736781 created "2021-06-07" @default.
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• W3165736781 date "2021-06-02" @default.
• W3165736781 modified "2023-10-12" @default.
• W3165736781 title "RetFluidNet: Retinal Fluid Segmentation for SD-OCT Images Using Convolutional Neural Network" @default.
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• W3165736781 doi "https://doi.org/10.1007/s10278-021-00459-w" @default.
• W3165736781 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8329142" @default.
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