Quantification of External Limiting Membrane Disruption Caused by Diabetic Macular Edema from SD-OCT
Quantification of External Limiting Membrane Disruption Caused by Diabetic Macular Edema from SD-OCT
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DOI：
10.1167/iovs.12-10083
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发表时间：
2012-12-01
影响因子：
4.4
通讯作者：
Abramoff, Michael D.
中科院分区：
文献类型：
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作者：
Chen, Xinjian;Zhang, Li;Abramoff, Michael D.
目的。谱域光学相干断层扫描 (SD-OCT) 上的外界膜 (ELM) 完整性破坏与糖尿病黄斑水肿 (DME) 患者视力下降有关。然而，不存在检测 ELM 和/或从 SD-OCT 确定其完整性的自动化方法。包括 16 名被诊断患有临床显着 DME (CSME) 的受试者，并接受了以黄斑为中心的 SD-OCT（512 x 19 x 496 体素）。还扫描了 16 名没有视网膜增厚且视力正常的受试者（200 x 200 x 1024 体素）。 ELM 破坏的自动量化如下实现。首先，使用我们的标准 3-D 图形搜索方法自动分割 11 个表面，并且基于分割的视网膜色素上皮 (RPE) 层将包含 ELM 区域的表面 6 和 11 之间的子体积展平。第二种基于边缘的图形搜索表面检测方法对紧邻 RPE“上方”的 ELM 区域进行分段，并且根据 ELM 表面附近的六个纹理特征将每个 ELM A 扫描分类为中断或非中断。在破坏分类过程中考虑了血管轮廓，以避免 ELM 破坏的错误检测。结果。在患有 CSME 的受试者中，存在大面积破坏的 ELM。在正常受试者中，ELM 基本上完好无损。正常和 CSME 受试者检测到的破坏区域体积的平均值和 95% 置信区间 (CI) 分别为平均值（正常） = 0.00087 mm(3) 和 CInormal = (0.00074, 0.00100)，平均值 (CSME) = 0.00461 mm(3) 和 CICSME = (0.00347, 0.00576) mm(3)，分别。结论。在这项初步研究中，我们能够证明 ELM 破坏的自动定量是可行的，并且可以区分正常受试者中的连续 ELM 和 CSME 受试者中被破坏的 ELM。我们已经开始确定定量 ELM 破坏标记与接受 CSME 治疗的患者视觉结果的关系。 （投资眼科可见科学。2012 年；53：8042-8048）DOI：10.1167/iovs.12-10083
PURPOSE. Disruption of external limiting membrane (ELM) integrity on spectral-domain optical coherence tomography (SD-OCT) is associated with lower visual acuity outcomes in patients suffering from diabetic macular edema (DME). However, no automated methods to detect ELM and/or determine its integrity from SD-OCT exist.METHODS. Sixteen subjects diagnosed with clinically significant DME (CSME) were included and underwent macula-centered SD-OCT (512 x 19 x 496 voxels). Sixteen subjects without retinal thickening and normal acuity were also scanned (200 x 200 x 1024 voxels). Automated quantification of ELM disruption was achieved as follows. First, 11 surfaces were automatically segmented using our standard 3-D graph-search approach, and the subvolume between surface 6 and 11 containing the ELM region was flattened based on the segmented retinal pigment epithelium (RPE) layer. A second, edge-based graph-search surface-detection method segmented the ELM region in close proximity "above" the RPE, and each ELM A-scan was classified as disrupted or nondisrupted based on six texture features in the vicinity of the ELM surface. The vessel silhouettes were considered in the disruption classification process to avoid false detections of ELM disruption.RESULTS. In subjects with CSME, large areas of disrupted ELM were present. In normal subjects, ELM was largely intact. The mean and 95% confidence interval (CI) of the detected disruption area volume for normal and CSME subjects were mean(normal) = 0.00087 mm(3) and CInormal = (0.00074, 0.00100), and mean(CSME) = 0.00461 mm(3) and CICSME = (0.00347, 0.00576) mm(3), respectively.CONCLUSIONS. In this preliminary study, we were able to show that automated quantification of ELM disruption is feasible and can differentiate continuous ELM in normal subjects from disrupted ELM in subjects with CSME. We have started determining the relationships of quantitative ELM disruption markers to visual outcome in patients undergoing treatment for CSME. (Invest Ophthalmol Vis Sci. 2012;53:8042-8048) DOI: 10.1167/iovs.12-10083