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

arXiv: https://arxiv.org/abs/2006.02569 · HTML: https://mlsanity.com/paper/2006.02569/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [DISCOVER: 2-D Multiview Summarization of Optical Coherence Tomography Angiography for Automatic Diabetic Retinopathy Diagnosis](https://mlsanity.com/paper/2401.05137/) (2024)
- [Towards data-driven filters in Paraview](https://mlsanity.com/paper/2108.05196/) (2021)
- [ADC-Net: An Open-Source Deep Learning Network for Automated Dispersion Compensation in Optical Coherence Tomography](https://mlsanity.com/paper/2201.12625/) (2022)
- [Interpretable Diabetic Retinopathy Diagnosis based on Biomarker Activation Map](https://mlsanity.com/paper/2212.06299/) (2022)
- [Denoising, segmentation and volumetric rendering of optical coherence tomography angiography (OCTA) image using deep learning techniques: a review](https://mlsanity.com/paper/2502.14935/) (2025)
- [Deep Learning-assisted AMD Staging based on OCT and OCT Angiography](https://mlsanity.com/paper/2606.05379/) (2026)

## Abstract (arXiv)

Purpose: We proposed a deep convolutional neural network (CNN), named Retinal Fluid Segmentation Network (ReF-Net) to segment volumetric retinal fluid on optical coherence tomography (OCT) volume. Methods: 3 x 3-mm OCT scans were acquired 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-like architecture was constructed to detect and segment the retinal fluid. Cross-sectional OCT and angiography (OCTA) scans were used for training and testing ReF-Net. The effect of including OCTA data for retinal fluid segmentation was investigated in this study. Volumetric retinal fluid can be constructed using the output of ReF-Net. Area-under-Receiver-Operating-Characteristic-curve (AROC), intersection-over-union (IoU), and F1-score were calculated to evaluate the performance 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 structural OCT. ReF-Net also shows strong robustness to shadow artifacts. Volumetric retinal fluid can provide more comprehensive information than the 2D area, whether cross-sectional or en face projections. Conclusions: A deep-learning-based method can accurately segment retinal fluid volumetrically on OCT/OCTA scans with strong robustness to shadow artifacts. OCTA data can improve retinal fluid segmentation. Volumetric representations of retinal fluid are superior to 2D projections. Translational Relevance: Using a deep learning method to segment retinal fluid volumetrically has the potential to improve the diagnostic accuracy of diabetic macular edema by OCT systems.