Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
IEEE Trans Biomed Eng. 2012 Apr;59(4):1109-14. doi: 10.1109/TBME.2012.2184759. Epub 2012 Jan 16.
Cystoid macular edema (CME) is observed in a variety of ocular disorders and is strongly associated with vision loss. Optical coherence tomography (OCT) provides excellent visualization of cystoid fluid, and can assist clinicians in monitoring the progression of CME. Quantitative tools for assessing CME may lead to better metrics for choosing treatment protocols. To address this need, this paper presents a fully automated retinal cyst segmentation technique for OCT image stacks acquired from a commercial scanner. The proposed method includes a computationally fast bilateral filter for speckle denoising while maintaining CME boundaries. The proposed technique was evaluated in images from 16 patients with vitreoretinal disease and three controls. The average sensitivity and specificity for the classification of cystoid regions in CME patients were found to be 91% and 96%, respectively, and the retinal volume occupied by cystoid fluid obtained by the algorithm was found to be accurate within a mean and median volume fraction of 1.9% and 0.8%, respectively.
囊样黄斑水肿（CME）可见于多种眼部疾病，与视力丧失密切相关。光学相干断层扫描（OCT）可极好地显示囊样液，有助于临床医生监测 CME 的进展。用于评估 CME 的定量工具可能会为选择治疗方案提供更好的指标。为满足这一需求，本研究提出了一种用于从商业扫描仪获取的 OCT 图像堆栈的全自动视网膜囊样分割技术。该方法包括一种计算快速的双边滤波器，用于在保持 CME 边界的同时进行斑点去噪。在来自 16 名患有玻璃体视网膜疾病的患者和 3 名对照者的图像中评估了该技术。结果发现，用于分类 CME 患者囊样区域的方法的平均灵敏度和特异性分别为 91%和 96%，并且算法获得的囊样液占据的视网膜体积在平均和中位数体积分数分别为 1.9%和 0.8%的范围内具有较高的准确性。
