Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula.
Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula.
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DOI：
10.1109/tmi.2010.2047023
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发表时间：
2010-06
影响因子：
10.6
通讯作者：
Sonka M
中科院分区：
文献类型：
--
作者：
Quellec G;Lee K;Dolejsi M;Garvin MK;Abràmoff MD;Sonka M
光学相干断层扫描（OCT）正在成为视网膜眼部疾病无创评估的最重要方式之一。随着采集的 OCT 体积数量的增加，自动化 OCT 图像分析变得越来越重要。在本文中，报告了一种在频域 OCT (SD-OCT) 体积中自动表征正常黄斑外观的方法以及局部视网膜异常检测的通用方法。首先自动分割 10 个视网膜内层，并将 3D 图像数据集展平以消除基于运动的伪影。从平坦化的 OCT 数据中，每层局部提取 23 个特征，以表征整个黄斑的纹理和厚度特性。层特定特征变化的正常范围源自描绘正常视网膜的 13 个 SD-OCT 体积。然后通过对正常外观和相关视网膜测量之间的局部差异进行分类来检测异常。该方法用于确定 78 个 SD-OCT 体积中充满液体的区域——SEAD（症状性渗出物相关紊乱）的足迹，这些区域来自 23 名患有脉络膜新生血管 (CNV)、视网膜内和视网膜下液体和色素上皮脱离的重复成像患者。自动 SEAD 足迹检测方法根据使用交互式 3-D SEAD 分割方法获得的独立标准进行了验证。对于垂直、跨层、黄斑柱的分类，获得了 0.961 ± 0.012 的受试者工作特征曲线下面积。对同一天从同一只眼睛获得的 12 对 OCT 体积进行的研究表明，自动化方法的可重复性与人类专家的可重复性相当。这项工作表明，可以从 SD-OCT 扫描中提取有用的 3D 纹理信息，并与正常视网膜的解剖图谱一起用于重要的临床应用。
Optical coherence tomography (OCT) is becoming one of the most important modalities for the noninvasive assessment of retinal eye diseases. As the number of acquired OCT volumes increases, automating the OCT image analysis is becoming increasingly relevant. In this paper, a method for automated characterization of the normal macular appearance in spectral domain OCT (SD-OCT) volumes is reported together with a general approach for local retinal abnormality detection. Ten intraretinal layers are first automatically segmented and the 3-D image dataset flattened to remove motion-based artifacts. From the flattened OCT data, 23 features are extracted in each layer locally to characterize texture and thickness properties across the macula. The normal ranges of layer-specific feature variations have been derived from 13 SD-OCT volumes depicting normal retinas. Abnormalities are then detected by classifying the local differences between the normal appearance and the retinal measures in question. This approach was applied to determine footprints of fluid-filled regions—SEADs (Symptomatic Exudate-Associated Derangements)—in 78 SD-OCT volumes from 23 repeatedly imaged patients with choroidal neovascularization (CNV), intra-, and sub-retinal fluid and pigment epithelial detachment. The automated SEAD footprint detection method was validated against an independent standard obtained using an interactive 3-D SEAD segmentation approach. An area under the receiver-operating characteristic curve of 0.961 ± 0.012 was obtained for the classification of vertical, cross-layer, macular columns. A study performed on 12 pairs of OCT volumes obtained from the same eye on the same day shows that the repeatability of the automated method is comparable to that of the human experts. This work demonstrates that useful 3-D textural information can be extracted from SD-OCT scans and—together with an anatomical atlas of normal retinas—can be used for clinically important applications.