Wang Jie, Hormel Tristan T, Gao Liqin, Zang Pengxiao, Guo Yukun, Wang Xiaogang, Bailey Steven T, Jia Yali
Casey Eye Institute, Oregon Health & Science University, Portland, OR 97239, USA.
Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA.
Biomed Opt Express. 2020 Jan 14;11(2):927-944. doi: 10.1364/BOE.379977. eCollection 2020 Feb 1.
Accurate identification and segmentation of choroidal neovascularization (CNV) is essential for the diagnosis and management of exudative age-related macular degeneration (AMD). Projection-resolved optical coherence tomographic angiography (PR-OCTA) enables both cross-sectional and visualization of CNV. However, CNV identification and segmentation remains difficult even with PR-OCTA due to the presence of residual artifacts. In this paper, a fully automated CNV diagnosis and segmentation algorithm using convolutional neural networks (CNNs) is described. This study used a clinical dataset, including both scans with and without CNV, and scans of eyes with different pathologies. Furthermore, no scans were excluded due to image quality. In testing, all CNV cases were diagnosed from non-CNV controls with 100% sensitivity and 95% specificity. The mean intersection over union of CNV membrane segmentation was as high as 0.88. By enabling fully automated categorization and segmentation, the proposed algorithm should offer benefits for CNV diagnosis, visualization monitoring.
准确识别和分割脉络膜新生血管（CNV）对于渗出性年龄相关性黄斑变性（AMD）的诊断和治疗至关重要。投影分辨光学相干断层扫描血管造影（PR-OCTA）能够实现CNV的横断面成像和可视化。然而，由于残留伪影的存在，即使使用PR-OCTA，CNV的识别和分割仍然困难。本文描述了一种使用卷积神经网络（CNN）的全自动CNV诊断和分割算法。本研究使用了一个临床数据集，包括有CNV和无CNV的扫描，以及患有不同病理状况眼睛的扫描。此外，没有因图像质量而排除任何扫描。在测试中，所有CNV病例均从非CNV对照中诊断出来，灵敏度为100%，特异性为95%。CNV膜分割的平均交并比高达0.88。通过实现全自动分类和分割，所提出的算法应为CNV诊断、可视化监测带来益处。
