@article{10.1167/tvst.9.2.54,
    author = {Guo, Yukun and Hormel, Tristan T. and Xiong, Honglian and Wang, Jie and Hwang, Thomas S. and Jia, Yali},
    title = "{Automated Segmentation of Retinal Fluid Volumes From Structural and Angiographic Optical Coherence Tomography Using Deep Learning}",
    journal = {Translational Vision Science & Technology},
    volume = {9},
    number = {2},
    pages = {54-54},
    year = {2020},
    month = {10},
    abstract = "{   We proposed a deep convolutional neural network (CNN), named Retinal Fluid Segmentation Network (ReF-Net), to segment retinal fluid in diabetic macular edema (DME) in optical coherence tomography (OCT) volumes.    The 3- × 3-mm OCT scans were acquired on one eye by a 70-kHz OCT commercial AngioVue system (RTVue-XR; Optovue, Inc., Fremont, CA, USA) from 51 participants in a clinical diabetic retinopathy (DR) study (45 with retinal edema and six healthy controls, age 61.3 ± 10.1 (mean ± SD), 33\\% female, and all DR cases were diagnosed as severe NPDR or PDR). 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, intersection-over-union (IoU), and F1-score were calculated to evaluate the performance of ReF-Net.    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 two-dimensional (2D) area, whether cross-sectional or en face projections.    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.    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.  }",
    issn = {2164-2591},
    doi = {10.1167/tvst.9.2.54},
    url = {https://doi.org/10.1167/tvst.9.2.54},
    eprint = {https://arvojournals.org/arvo/content\_public/journal/tvst/938366/i2164-2591-9-2-54\_1602136035.84543.pdf},
}