Comprehensive Evaluation of OCT-based Automated Segmentation of Retinal Layer, Fluid and Hyper-Reflective Foci: Impact on Diabetic Retinopathy Severity Assessment

• URL: http://arxiv.org/abs/2503.01248v3
• Date: Fri, 11 Apr 2025 03:23:52 GMT
• Title: Comprehensive Evaluation of OCT-based Automated Segmentation of Retinal Layer, Fluid and Hyper-Reflective Foci: Impact on Diabetic Retinopathy Severity Assessment
• Authors: S. Chen, D. Ma, M. Raviselvan, S. Sundaramoorthy, K. Popuri, M. J. Ju, M. V. Sarunic, D. Ratra, M. F. Beg,
• Abstract summary: Diabetic retinopathy (DR) is a leading cause of vision loss, requiring early and accurate assessment to prevent irreversible damage.<n>This study proposes an active-learning-based deep learning pipeline for automated segmentation of retinal layers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diabetic retinopathy (DR) is a leading cause of vision loss, requiring early and accurate assessment to prevent irreversible damage. Spectral Domain Optical Coherence Tomography (SD-OCT) enables high-resolution retinal imaging, but automated segmentation performance varies, especially in cases with complex fluid and hyperreflective foci (HRF) patterns. This study proposes an active-learning-based deep learning pipeline for automated segmentation of retinal layers, fluid, and HRF, using four state-of-the-art models: U-Net, SegFormer, SwinUNETR, and VM-UNet, trained on expert-annotated SD-OCT volumes. Segmentation accuracy was evaluated with five-fold cross-validation, and retinal thickness was quantified using a K-nearest neighbors algorithm and visualized with Early Treatment Diabetic Retinopathy Study (ETDRS) maps. SwinUNETR achieved the highest overall accuracy (DSC = 0.7719; NSD = 0.8149), while VM-UNet excelled in specific layers. Structural differences were observed between non-proliferative and proliferative DR, with layer-specific thickening correlating with visual acuity impairment. The proposed framework enables robust, clinically relevant DR assessment while reducing the need for manual annotation, supporting improved disease monitoring and treatment planning.

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