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Purpose: We proposed a deep convolutional neural network (CNN), named RetinalFluid Segmentation Network (ReF-Net) to segment volumetric retinal fluid onoptical coherence tomography (OCT) volume. Methods: 3 x 3-mm OCT scans wereacquired on one eye by a 70-kHz OCT commercial AngioVue system (RTVue-XR;Optovue, Inc.) from 51 participants in a clinical diabetic retinopathy (DR)study (45 with retinal edema and 6 healthy controls). A CNN with U-Net-likearchitecture was constructed to detect and segment the retinal fluid.Cross-sectional OCT and angiography (OCTA) scans were used for training andtesting ReF-Net. The effect of including OCTA data for retinal fluidsegmentation was investigated in this study. Volumetric retinal fluid can beconstructed using the output of ReF-Net.Area-under-Receiver-Operating-Characteristic-curve (AROC),intersection-over-union (IoU), and F1-score were calculated to evaluate theperformance of ReF-Net. Results: ReF-Net shows high accuracy (F1 = 0.864 +/-0.084) in retinal fluid segmentation. The performance can be further improved(F1 = 0.892 +/- 0.038) by including information from both OCTA and structuralOCT. ReF-Net also shows strong robustness to shadow artifacts. Volumetricretinal fluid can provide more comprehensive information than the 2D area,whether cross-sectional or en face projections. Conclusions: Adeep-learning-based method can accurately segment retinal fluid volumetricallyon OCT/OCTA scans with strong robustness to shadow artifacts. OCTA data canimprove retinal fluid segmentation. Volumetric representations of retinal fluidare superior to 2D projections. Translational Relevance: Using a deep learningmethod to segment retinal fluid volumetrically has the potential to improve thediagnostic accuracy of diabetic macular edema by OCT systems.

Automated segmentation of retinal fluid volumes from structural and angiographic optical coherence tomography using deep learning