Automated Characterization of Pigment Epithelial Detachment by Optical Coherence Tomography
Automated Characterization of Pigment Epithelial Detachment by Optical Coherence Tomography
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DOI：
10.1167/iovs.11-8188
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发表时间：
2012-01-01
影响因子：
4.4
通讯作者：
Sadda, SriniVas R.
中科院分区：
文献类型：
--
作者：
Lee, Sun Young;Stetson, Paul F.;Sadda, SriniVas R.
目的.通过应用软件算法进行光谱域光学相干断层扫描（SD-OCT），评估色素上皮脱髓鞘（PED）自动分类的准确性。HD-OCT（Cirrus; Carl Zeiss Meditec，都柏林，CA）容积扫描（512 x 128），回顾性收集了33例年龄相关性黄斑变性（AMD，n = 28）或中心性浆液性脉络膜视网膜病变（CSCR，n = 5）患者的46只眼PED证据。在这些眼睛中，使用系统相关工具（Cirrus HD-OCT RPE Elevation Analysis; Carl Zeiss Meditec）自动检测168个PED。两名独立的、经认证的Doheny图像阅读中心（DIRC）OCT分级员通过检查B扫描将这些PED分为三类-浆液性、玻璃状或纤维血管性。人工分类结果是与自动分类进行比较的金标准。对于自动分类，在所有图像中对强度的个体间变化进行归一化。然后根据平均内部强度和内部强度的标准差将检测到的PED内的单个A扫描自动分类为三种类别之一：平均强度= 30但>= 60或平均强度>= 30且SD >= 30（纤维血管型）;或平均强度>= 60且SD < 30（核果样型）。然后，根据PED内的主要A扫描类型，将单个PED自动分类为相同的三个类别。对于混合PED（每种类型的许多A扫描），根据纤维血管A扫描的百分比计算新血管形成的风险指数。此外，根据每个PED与PED类别边界的数学距离计算每个PED的置信度指数。在168例PED中，DIRC分级者将16例归类为浆液性PED，88例归类为纤维血管性PED，64例归类为核果样PED。自动算法将14例分类为浆液性PED，96例为纤维血管性PED，58例为玻璃疣样PED。根据PED类型自动分类的敏感性和特异性值分别为浆液性88%和100%，纤维血管性76%和64%，玻璃疣样58%和81%。使用内部反射率特征对PED进行自动分类似乎对检测浆液性和纤维血管性PED敏感。PED的自动分类和量化可能是未来研究中根据风险对PED进行分层并可能预测晚期AMD风险的有用工具。（Invest Ophthalmol维斯科学。2012;53：164-170）DOI：10.1167/iovs.11-8188
PURPOSE. To assess the accuracy of automated classification of pigment epithelial detachments (PED) by using a software algorithm applied to spectral-domain optical coherence tomography (SD-OCT) scans.METHODS. HD-OCT (Cirrus; Carl Zeiss Meditec, Dublin, CA) volume scans (512 x 128) were retrospectively collected from 46 eyes of 33 patients with evidence of PED in the setting of age-related macular degeneration (AMD, n = 28) or central serous chorioretinopathy (CSCR, n = 5). In these eyes, 168 PEDs were automatically detected with a system-associated tool (Cirrus HD-OCT RPE Elevation Analysis; Carl Zeiss Meditec). Two independent, certified Doheny Image Reading Center (DIRC) OCT graders classified these PEDs into three categories-serous, drusenoid, or fibrovascular-via inspection of the B-scans. Manual classification results served as the gold standard for comparisons with automated classification. For automated classification, interindividual variation in intensities was normalized in all images. Individual A-scans within the detected PEDs were then automatically classified into one of three categories based on the mean internal intensity and the standard deviation of the internal intensity: mean intensity = 30 but >= 60 or mean intensity >= 30 and SD >= 30 (fibrovascular type); or mean intensity >= 60 and SD < 30 (drusenoid type). Individual PEDs were then automatically classified into the same three categories based on the predominant type of A-scan within the PED. For mixed PEDs (many A-scans of each type), a risk index for neovascularization was computed based on the percentage of fibrovascular A-scans. In addition, a confidence index was computed for each PED based on its mathematical distance from the PED category boundaries.RESULTS. Among the 168 PEDs, the DIRC graders classified 16 as serous, 88 as fibrovascular, and 64 as drusenoid PEDs. The automated algorithm classified 14 as serous, 96 as fibrovascular, and 58 as drusenoid PEDs. The sensitivity and specificity values for automated classification according to type of PED were 88% and 100% for serous, 76% and 64% for fibrovascular, and 58% and 81% for drusenoid, respectively.CONCLUSIONS. Automated classification of PEDs using internal reflectivity characteristics appears to be sensitive for detecting serous and fibrovascular PEDs. Automated classification and quantification of PEDs may be a useful tool in future studies for stratifying PEDs according to risk and possibly predicting the risk of advanced AMD. (Invest Ophthalmol Vis Sci. 2012;53:164-170) DOI:10.1167/iovs.11-8188