Xue Jie, Camino Acner, Bailey Steven T, Liu Xiyu, Li Dengwang, Jia Yali
Casey Eye Institute, Oregon Health & Science University, Portland, OR, 97239, USA.
School of Management Science and Engineering, Shandong Normal University, Jinan, 250014,China.
Biomed Opt Express. 2018 Jun 20;9(7):3208-3219. doi: 10.1364/BOE.9.003208. eCollection 2018 Jul 1.
Detecting and quantifying the size of choroidal neovascularization (CNV) is important for the diagnosis and assessment of neovascular age-related macular degeneration. Depth-resolved imaging of the retinal and choroidal vasculature by optical coherence tomography angiography (OCTA) has enabled the visualization of CNV. However, due to the prevalence of artifacts, it is difficult to segment and quantify the CNV lesion area automatically. We have previously described a saliency algorithm for CNV detection that could identify a CNV lesion area with 83% accuracy. However, this method works under the assumption that the CNV region is the most salient area for visual attention in the whole image and consequently, errors occur when this requirement is not met (e.g. when the lesion occupies a large portion of the image). Moreover, saliency image processing methods cannot extract the edges of the salient object very accurately. In this paper, we propose a novel and automatic CNV segmentation method based on an unsupervised and parallel machine learning technique named density cell-like P systems (DEC P systems). DEC P systems integrate the idea of a modified clustering algorithm into cell-like P systems. This method improved the accuracy of detection to 87.2% on 22 subjects and obtained clear boundaries of the CNV lesions.
检测和量化脉络膜新生血管（CNV）的大小对于新生血管性年龄相关性黄斑变性的诊断和评估至关重要。光学相干断层扫描血管造影（OCTA）对视网膜和脉络膜血管系统进行深度分辨成像，使得CNV得以可视化。然而，由于伪影普遍存在，自动分割和量化CNV病变区域较为困难。我们之前描述了一种用于CNV检测的显著性算法，该算法能够以83%的准确率识别CNV病变区域。然而，该方法的工作假设是CNV区域是整个图像中视觉注意力最显著的区域，因此，当不满足这一要求时（例如病变占据图像的很大一部分）就会出现错误。此外，显著性图像处理方法不能非常准确地提取显著对象的边缘。在本文中，我们提出了一种基于无监督并行机器学习技术——密度细胞类P系统（DEC P系统）的新型自动CNV分割方法。DEC P系统将改进的聚类算法的思想融入细胞类P系统。该方法在22名受试者上将检测准确率提高到了87.2%，并获得了CNV病变的清晰边界。
