论文标题
使用深学习的结构和血管造影光学相干断层扫描的视网膜流体体积的自动分割
Automated segmentation of retinal fluid volumes from structural and angiographic optical coherence tomography using deep learning
论文作者
论文摘要
目的：我们提出了一个深卷积神经网络（CNN），称为视网膜流体分割网络（REF-NET），以分段体积视网膜流体（OCT）体积（OCT）体积。方法：从51个临床糖尿病性视网膜病（DR）研究中的51名参与者（45个带有视网膜水肿和6个健康对照组）中的70 kHz OCT Angiovue系统（RTVUE-XR; Optovue，Inc。）在一只眼睛中获得了3 x 3毫米OCT扫描。构建了具有U-NET样结构的CNN，以检测和分段视网膜流体。横截面OCT和血管造影（八八）扫描用于训练和测试参考网络。在这项研究中研究了包括八八颗数据进行视网膜流体分割的效果。可以使用Ref-NET的输出来构建体积视网膜流体。计算出区域范围内的特征曲线（AROC），交叉点（IOU）和F1得分以评估Ref-NET的性能。结果：Ref-NET在视网膜流体分割中显示出很高的精度（F1 = 0.864 +/- 0.084）。通过包括来自八八八和结构OCT的信息，可以进一步提高性能（F1 = 0.892 +/- 0.038）。 ref-net还表现出对影子工件的强大鲁棒性。体积视网膜液可以提供比2D区域（无论是横截面还是面部预测）提供更多的全面信息。结论：一种基于深度学习的方法可以在OCT/OCTA扫描上精确地分割视网膜流体，具有强大的阴影伪像。八颗数据可以改善视网膜流体分割。视网膜流体的体积表示优于2D投影。翻译相关性：使用深度学习方法通过体积分割视网膜流体，有可能通过OCT系统提高糖尿病黄斑水肿的诊断准确性。
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.
