Guo Yukun, Hormel Tristan T, Xiong Honglian, Wang Jie, Hwang Thomas S, Jia Yali
Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA.
School of Physics and Optoelectronic Engineering, Foshan University, Foshan, Guangdong, China.
Transl Vis Sci Technol. 2020 Oct 8;9(2):54. doi: 10.1167/tvst.9.2.54. eCollection 2020 Oct.
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.
我们提出了一种名为视网膜积液分割网络（ReF-Net）的深度卷积神经网络（CNN），用于在光学相干断层扫描（OCT）容积中分割糖尿病性黄斑水肿（DME）中的视网膜积液。
在一项临床糖尿病视网膜病变（DR）研究中，使用70kHz的OCT商用AngioVue系统（RTVue-XR；美国加利福尼亚州弗里蒙特市Optovue公司）对51名参与者的一只眼睛进行3×3mm的OCT扫描（45例有视网膜水肿，6例健康对照，年龄61.3±10.1（均值±标准差），33%为女性，所有DR病例均被诊断为重度非增殖性糖尿病视网膜病变或增殖性糖尿病视网膜病变）。构建了一个具有类似U-Net架构的CNN来检测和分割视网膜积液。使用横断面OCT和血管造影（OCTA）扫描来训练和测试ReF-Net。本研究调查了纳入OCTA数据对视网膜积液分割的影响。可以使用ReF-Net的输出构建视网膜积液容积。计算受试者操作特征曲线下面积、交并比（IoU）和F1分数来评估ReF-Net的性能。
ReF-Net在视网膜积液分割中显示出高准确率（F1 = 0.864±0.084）。通过纳入OCTA和结构性OCT的信息，性能可进一步提高（F1 = 0.892±0.038）。ReF-Net对阴影伪影也具有很强的鲁棒性。视网膜积液容积比二维（2D）区域（无论是横断面还是正面投影）能提供更全面的信息。
一种基于深度学习的方法能够在OCT/OCTA扫描上准确地对视网膜积液进行容积分割，对阴影伪影具有很强的鲁棒性。OCTA数据可改善视网膜积液分割。视网膜积液的容积表示优于二维投影。
使用深度学习方法对视网膜积液进行容积分割有可能提高OCT系统对糖尿病性黄斑水肿的诊断准确性。
