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

• URL: http://arxiv.org/abs/2006.02569v1
• Date: Wed, 3 Jun 2020 22:55:47 GMT
• Title: Automated segmentation of retinal fluid volumes from structural and angiographic optical coherence tomography using deep learning
• Authors: Yukun Guo, Tristan T. Hormel, Honglian Xiong, Jie Wang, Thomas S. Hwang, Yali Jia
• Abstract summary: We proposed a deep convolutional neural network (CNN) named Retinal Fluid Network (ReF-Net) to segment volumetric retinal fluid on optical coherence tomography ( OCT) volume. Cross-sectional OCT and angiography ( OCTA) scans were used for training and testing ReF-Net. ReF-Net shows high accuracy (F1 = 0.864 +/- 0.084) in retinal fluid segmentation.
• Score: 2.041049231600541
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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