Doheny Eye Institute, University of Southern California, Los Angeles, CA, USA.
Invest Ophthalmol Vis Sci. 2013 Mar 7;54(3):1722-9. doi: 10.1167/iovs.12-10578.
Changes in the choroid, in particular its thickness, are believed to be of importance in the pathophysiology of a number of retinal diseases. The purpose of this study was to adapt the graph search algorithm to semiautomatically identify the choroidal layer in spectral-domain optical coherence tomography (SD-OCT) volume scans and compare its performance to manual delineation.
A graph-based multistage segmentation approach was used to identify the choroid, defined as the layer between the outer border of the RPE band and the choroid-sclera junction. Thirty randomly chosen macular SD-OCT (1024 × 37 × 496 voxels, Heidelberg Spectralis) volumes were obtained from 20 healthy subjects and 10 subjects with non-neovascular AMD. The positions of the choroidal borders and resultant thickness were compared with consensus manual delineation performed by two graders. For consistency of the statistical analysis, the left eyes were horizontally flipped in the x-direction.
The algorithm-defined position of the outer RPE border and choroid-sclera junction was consistent with the manual delineation, resulting in highly correlated choroidal thickness values with r = 0.91 to 0.93 for the healthy subjects and 0.94 for patients with non-neovascular AMD. Across all cases, the mean and absolute differences between the algorithm and manual segmentation for the outer RPE boundary was -0.74 ± 3.27 μm and 3.15 ± 3.07 μm; and for the choroid-sclera junction was -3.90 ± 15.93 μm and 21.39 ± 10.71 μm.
Excellent agreement was observed between the algorithm and manual choroidal segmentation in both normal eyes and those with non-neovascular AMD. The choroid was thinner in AMD eyes. Semiautomated choroidal thickness calculation may be useful for large-scale quantitative studies of the choroid.
脉络膜的变化,特别是其厚度,被认为在许多视网膜疾病的病理生理学中很重要。本研究的目的是改编图搜索算法,以半自动方式识别光谱域光学相干断层扫描(SD-OCT)容积扫描中的脉络膜层,并比较其性能与手动描绘。
使用基于图的多阶段分割方法来识别脉络膜,定义为 RPE 带的外边界和脉络膜-巩膜交界处之间的层。从 20 名健康受试者和 10 名非新生血管性 AMD 受试者中获得 30 个随机选择的黄斑 SD-OCT(1024×37×496 体素,海德堡 Spectralis)容积。比较了脉络膜边界的位置和所得厚度与由两名分级员进行的共识手动描绘。为了保持统计分析的一致性,将左眼在 x 方向上水平翻转。
算法定义的外 RPE 边界和脉络膜-巩膜交界处的位置与手动描绘一致,导致健康受试者的脉络膜厚度值高度相关,r 值为 0.91 至 0.93,而非新生血管性 AMD 患者的 r 值为 0.94。在所有病例中,算法与手动分割的外 RPE 边界的平均值和绝对差异为-0.74±3.27μm 和 3.15±3.07μm;脉络膜-巩膜交界处为-3.90±15.93μm 和 21.39±10.71μm。
在正常眼和非新生血管性 AMD 眼中,算法与手动脉络膜分割之间观察到极好的一致性。AMD 眼的脉络膜较薄。半自动脉络膜厚度计算可能对大规模定量研究脉络膜有用。