Department of Bioengineering, University of Washington, Seattle, Washington, USA.
Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, USA.
Am J Ophthalmol. 2020 May;213:161-176. doi: 10.1016/j.ajo.2020.02.003. Epub 2020 Feb 12.
To demonstrate the proper use of the Phansalkar local thresholding method (Phansalkar method) in choriocapillaris (CC) quantification with optical coherence tomography angiography (OCTA).
Retrospective, observational case series.
Swept source OCTA imaging was performed using 3×3 mm and 6×6 mm scanning patterns. The CC slab was extracted after semiautomatic segmentation of the retinal pigment epithelium/Bruch membrane complex. Retinal projection artifacts were removed before further analysis, and CC OCTA images from drusen eyes were compensated using a previously published strategy. CC flow deficits (FDs) were segmented with 2 previously published algorithms: the fuzzy C-means approach (FCM method) and the Phansalkar method. With the Phansalkar method, different parameters were tested and a local window radius of 1 to 15 pixels was used. FD density, mean FD size, and FD number were calculated for comparison.
Six normal eyes from 6 subjects and 6 eyes with drusen secondary to age-related macular degeneration from 6 subjects were analyzed. With both 3×3 mm and 6×6 mm scans from all eyes, the FD metrics were highly dependent on the selection of the local window radius when using the Phansalkar method. Larger window radii resulted in higher FD density values. FD number increased with the increase in the window radius but then decreased, with an inflection point at about 1 to 2 intercapillary distances. Mean FD size decreased then increased with increasing window radii.
Multiple parameters, especially the local window radius, should be optimized before using the Phansalkar method for the quantification of CC FDs with OCTA imaging. It is recommended that the proper use of the Phansalkar method should include the selection of the window radius that is related to the expected intercapillary distance in normal eyes.
展示 Phansalkar 局部阈值法（Phansalkar 法）在脉络膜毛细血管（CC）定量光学相干断层扫描血管造影（OCTA）中的正确应用。
回顾性、观察性病例系列。
使用 3×3mm 和 6×6mm 扫描模式进行扫频源 OCTA 成像。在视网膜色素上皮/脉络膜膜复合体的半自动分割后提取 CC 板。在进一步分析之前，去除视网膜投影伪影，并用之前发表的策略补偿来自渗出性老年黄斑变性眼的 CC OCTA 图像。使用两种先前发表的算法分段 CC 血流缺损（FD）：模糊 C-均值方法（FCM 方法）和 Phansalkar 方法。使用 Phansalkar 方法测试了不同的参数，并使用 1 到 15 像素的局部窗口半径。计算 FD 密度、平均 FD 大小和 FD 数量进行比较。
分析了来自 6 名受试者的 6 只正常眼和来自 6 名受试者的 6 只渗出性老年黄斑变性眼。使用所有眼的 3×3mm 和 6×6mm 扫描，当使用 Phansalkar 方法时，FD 指标高度依赖于局部窗口半径的选择。较大的窗口半径导致较高的 FD 密度值。随着窗口半径的增加，FD 数量增加，但随后减少，在约 1 到 2 个毛细血管间距处出现拐点。平均 FD 大小随窗口半径的增加而先减小后增大。
在使用 OCTA 成像对 CC FD 进行定量分析之前，应优化多个参数，特别是局部窗口半径。建议正确使用 Phansalkar 法应包括选择与正常眼中预期毛细血管间距相关的窗口半径。
