Department of Radiology, Stanford University, Stanford, CA 94305, USA; School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Med Image Anal. 2013 Dec;17(8):1058-72. doi: 10.1016/j.media.2013.06.003. Epub 2013 Jul 2.
Spectral domain optical coherence tomography (SD-OCT) is a useful tool for the visualization of drusen, a retinal abnormality seen in patients with age-related macular degeneration (AMD); however, objective assessment of drusen is thwarted by the lack of a method to robustly quantify these lesions on serial OCT images. Here, we describe an automatic drusen segmentation method for SD-OCT retinal images, which leverages a priori knowledge of normal retinal morphology and anatomical features. The highly reflective and locally connected pixels located below the retinal nerve fiber layer (RNFL) are used to generate a segmentation of the retinal pigment epithelium (RPE) layer. The observed and expected contours of the RPE layer are obtained by interpolating and fitting the shape of the segmented RPE layer, respectively. The areas located between the interpolated and fitted RPE shapes (which have nonzero area when drusen occurs) are marked as drusen. To enhance drusen quantification, we also developed a novel method of retinal projection to generate an en face retinal image based on the RPE extraction, which improves the quality of drusen visualization over the current approach to producing retinal projections from SD-OCT images based on a summed-voxel projection (SVP), and it provides a means of obtaining quantitative features of drusen in the en face projection. Visualization of the segmented drusen is refined through several post-processing steps, drusen detection to eliminate false positive detections on consecutive slices, drusen refinement on a projection view of drusen, and drusen smoothing. Experimental evaluation results demonstrate that our method is effective for drusen segmentation. In a preliminary analysis of the potential clinical utility of our methods, quantitative drusen measurements, such as area and volume, can be correlated with the drusen progression in non-exudative AMD, suggesting that our approach may produce useful quantitative imaging biomarkers to follow this disease and predict patient outcome.
光谱域光相干断层扫描（SD-OCT）是一种用于观察年龄相关性黄斑变性（AMD）患者视网膜中出现的玻璃膜疣的有用工具；然而，由于缺乏一种方法来对连续的 OCT 图像中的这些病变进行稳健地定量评估，因此对玻璃膜疣的客观评估受到了阻碍。在这里，我们描述了一种用于 SD-OCT 视网膜图像的自动玻璃膜疣分割方法，该方法利用了正常视网膜形态和解剖特征的先验知识。位于视网膜神经纤维层（RNFL）下方的高反射和局部连接的像素用于生成视网膜色素上皮（RPE）层的分割。通过分别对分割的 RPE 层的形状进行插值和拟合，得到观察到的和期望的 RPE 层轮廓。位于插值和拟合的 RPE 形状之间的区域（当出现玻璃膜疣时，这些区域具有非零面积）被标记为玻璃膜疣。为了增强玻璃膜疣的定量评估，我们还开发了一种新的视网膜投影方法，根据 RPE 提取生成基于 RPE 提取的 En Face 视网膜图像，与基于总和体素投影（SVP）的 SD-OCT 图像产生视网膜投影的当前方法相比，该方法提高了玻璃膜疣可视化的质量，并提供了一种获取 En Face 投影中玻璃膜疣定量特征的方法。通过几个后处理步骤对分割的玻璃膜疣进行细化，包括在连续切片上消除假阳性检测的玻璃膜疣检测、玻璃膜疣投影视图上的玻璃膜疣细化以及玻璃膜疣平滑。实验评估结果表明，我们的方法对于玻璃膜疣分割是有效的。在对我们的方法的潜在临床实用性进行的初步分析中，定量玻璃膜疣测量（如面积和体积）可以与非渗出性 AMD 中的玻璃膜疣进展相关联，这表明我们的方法可能会产生有用的定量成像生物标志物来跟踪这种疾病并预测患者的结果。
