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Automated drusen segmentation and quantification in SD-OCT images

机译：SD-OCT图像中自动化的德定分段和定量

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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.
机译：光谱域光学相干断层扫描（SD-OCT）是司令部可视化的有用工具，在患有年龄相关的黄斑变性（AMD）的患者中看到的视网膜异常;然而，由于缺乏在串行OCT图像上稳健地量化这些病变的方法，因此对Drusen的客观评估被挫败。在这里，我们描述了一种用于SD-OCT视网膜图像的自动漂移分割方法，其利用正常视网膜形态和解剖学特征的先验知识。位于视网膜神经纤维层（RNFL）下方的高反射和局部连接像素用于产生视网膜颜料上皮（RPE）层的分段。通过分别内插和装配分段的RPE层的形状来获得RPE层的观察和预期轮廓。位于插值和拟合的RPE形状（当发生博彩时的非零区域）的区域被标记为Drusen。为了增强德鲁森定量，我们还开发了一种基于RPE提取产生了en面视网膜图像的新型视网膜投影方法，这提高了基于a的SD-OCT图像产生视网膜投影的博客可视化的质量。 Summed-Voxel投影（SVP），并且它提供了在en面投影中获得德鲁森的定量特征的方法。通过几个后处理步骤，德鲁森检测以消除连续切片的伪阳性检测，德鲁森的投影视图上的伪阳性检测，探测器的探测器，探测器的探测器和德鲁森平滑的投影视图。实验评价结果表明，我们的方法对德鲁森分割是有效的。在对我们方法的潜在临床效用的初步分析中，如面积和体积，例如面积和体积，可以与非渗出的AMD中的博彩进展相关，这表明我们的方法可能产生有用的定量成像生物标志物以遵循这种疾病并预测患者结果。

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