Wang Jie, Zhang Miao, Pechauer Alex D, Liu Liang, Hwang Thomas S, Wilson David J, Li Dengwang, Jia Yali
Casey Eye Institute, Oregon Health & Science University, Portland, OR 97239, USA; Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, Shandong Normal University, Jinan, 250014, China.
Casey Eye Institute, Oregon Health & Science University, Portland, OR 97239, USA.
Biomed Opt Express. 2016 Mar 30;7(4):1577-89. doi: 10.1364/BOE.7.001577. eCollection 2016 Apr 1.
We propose a novel automated volumetric segmentation method to detect and quantify retinal fluid on optical coherence tomography (OCT). The fuzzy level set method was introduced for identifying the boundaries of fluid filled regions on B-scans (x and y-axes) and C-scans (z-axis). The boundaries identified from three types of scans were combined to generate a comprehensive volumetric segmentation of retinal fluid. Then, artefactual fluid regions were removed using morphological characteristics and by identifying vascular shadowing with OCT angiography obtained from the same scan. The accuracy of retinal fluid detection and quantification was evaluated on 10 eyes with diabetic macular edema. Automated segmentation had good agreement with manual segmentation qualitatively and quantitatively. The fluid map can be integrated with OCT angiogram for intuitive clinical evaluation.
我们提出了一种新颖的自动容积分割方法，用于在光学相干断层扫描（OCT）上检测和量化视网膜积液。引入了模糊水平集方法来识别B扫描（x轴和y轴）和C扫描（z轴）上积液区域的边界。将从三种类型扫描中识别出的边界进行合并，以生成视网膜积液的全面容积分割。然后，利用形态学特征并通过从同一次扫描获得的OCT血管造影识别血管阴影，去除伪影性积液区域。在10只患有糖尿病性黄斑水肿的眼睛上评估了视网膜积液检测和量化的准确性。自动分割在定性和定量方面与手动分割具有良好的一致性。积液图可与OCT血管造影图整合，以进行直观的临床评估。
