PDF(Pubmed)
Abstract：
UNASSIGNED: To characterize the effect of sampling window size on maps of foveal cone density derived from adaptive optics scanning light ophthalmoscope (AOSLO) images of the cone mosaic.
UNASSIGNED: Forty-four AOSLO-derived montages of the foveal cone mosaic (300 x 300µm) were used for this study (from 44 individuals with normal vision). Cone photoreceptor coordinates were semi-automatically identified by one experienced grader. From these coordinates, cone density matrices across each foveal montage were derived using 10 different sampling window sizes containing 5, 10, 15, 20, 40, 60, 80, 100, 150, or 200 cones. For all 440 density matrices, we extracted the location and value of peak cone density (PCD), the cone density centroid (CDC) location, and cone density at the CDC.
UNASSIGNED: Across all window sizes, PCD values were larger than those extracted at the CDC location, though the difference between these density values decreased as the sampling window size increased (p<0.0001). Overall, both PCD (r=-0.8099, p=0.0045) and density at the CDC (r=-0.7596, p=0.0108) decreased with increasing sampling window size. This reduction was more pronounced for PCD, with a 27.8% lower PCD value on average when using the 200-cone versus the 5-cone window (compared to only a 3.5% reduction for density at the CDC between these same window sizes). While the PCD and CDC locations did not occur at the same location within a given montage, there was no significant relationship between this PCD-CDC offset and sampling window size (p=0.8919). The CDC location was less variable across sampling windows, with an average per-participant 95% confidence ellipse area across the 10 window sizes of 47.56µm² (compared to 844.10µm² for the PCD location, p<0.0001).
UNASSIGNED: CDC metrics appear more stable across varying sampling window sizes than PCD metrics. Understanding how density values change according to the method used to sample the cone mosaic may facilitate comparing cone density data across different studies.
UNASSIGNED: Forty-four AOSLO-derived montages of the foveal cone mosaic (300 x 300µm) were used for this study (from 44 individuals with normal vision). Cone photoreceptor coordinates were semi-automatically identified by one experienced grader. From these coordinates, cone density matrices across each foveal montage were derived using 10 different sampling window sizes containing 5, 10, 15, 20, 40, 60, 80, 100, 150, or 200 cones. For all 440 density matrices, we extracted the location and value of peak cone density (PCD), the cone density centroid (CDC) location, and cone density at the CDC.
UNASSIGNED: Across all window sizes, PCD values were larger than those extracted at the CDC location, though the difference between these density values decreased as the sampling window size increased (p<0.0001). Overall, both PCD (r=-0.8099, p=0.0045) and density at the CDC (r=-0.7596, p=0.0108) decreased with increasing sampling window size. This reduction was more pronounced for PCD, with a 27.8% lower PCD value on average when using the 200-cone versus the 5-cone window (compared to only a 3.5% reduction for density at the CDC between these same window sizes). While the PCD and CDC locations did not occur at the same location within a given montage, there was no significant relationship between this PCD-CDC offset and sampling window size (p=0.8919). The CDC location was less variable across sampling windows, with an average per-participant 95% confidence ellipse area across the 10 window sizes of 47.56µm² (compared to 844.10µm² for the PCD location, p<0.0001).
UNASSIGNED: CDC metrics appear more stable across varying sampling window sizes than PCD metrics. Understanding how density values change according to the method used to sample the cone mosaic may facilitate comparing cone density data across different studies.
摘要：
■表征采样窗口大小对从自适应光学扫描光检眼镜（AOSLO）图像得到的中央凹锥体密度图的影响。
■本研究使用了44个AOSLO衍生的中央凹锥马赛克（300x300µm）蒙太奇（来自44个视力正常的个体）。锥形感光体坐标由一位有经验的分级者半自动识别。从这些坐标来看,使用包含5,10,15,20,40,60,80,100,150或200个锥体的10个不同的采样窗口大小,得出每个中央凹蒙太奇的锥体密度矩阵.对于所有440个密度矩阵，我们提取了峰值锥密度(PCD)的位置和值，圆锥密度质心(CDC)位置，和疾控中心的锥形密度。
■在所有窗口大小中，PCD值大于在CDC位置提取的值，尽管这些密度值之间的差异随着采样窗口大小的增加而减小（p<0.0001）。总的来说,PCD(r=-0.8099,p=0.0045)和CDC密度(r=-0.7596,p=0.0108)均随采样窗口大小的增加而降低.这种减少对于PCD更为明显，当使用200锥与5锥窗口时，PCD值平均降低27.8%（相比之下，在这些相同的窗口尺寸之间，CDC的密度仅降低3.5%）。虽然PCD和CDC位置没有发生在给定蒙太奇内的相同位置，PCD-CDC偏移与采样窗口大小之间没有显著关系(p=0.8919).CDC位置在采样窗口中的变化较小，在10个窗口大小47.56µm²（与PCD位置的844.10µm²相比，每个参与者的平均95%置信度椭圆面积，p<0.0001)。
■在不同的采样窗口大小下，CDC指标比PCD指标更稳定。了解密度值如何根据用于对锥体马赛克进行采样的方法而变化可以有助于比较不同研究中的锥体密度数据。
■本研究使用了44个AOSLO衍生的中央凹锥马赛克（300x300µm）蒙太奇（来自44个视力正常的个体）。锥形感光体坐标由一位有经验的分级者半自动识别。从这些坐标来看,使用包含5,10,15,20,40,60,80,100,150或200个锥体的10个不同的采样窗口大小,得出每个中央凹蒙太奇的锥体密度矩阵.对于所有440个密度矩阵，我们提取了峰值锥密度(PCD)的位置和值，圆锥密度质心(CDC)位置，和疾控中心的锥形密度。
■在所有窗口大小中，PCD值大于在CDC位置提取的值，尽管这些密度值之间的差异随着采样窗口大小的增加而减小（p<0.0001）。总的来说,PCD(r=-0.8099,p=0.0045)和CDC密度(r=-0.7596,p=0.0108)均随采样窗口大小的增加而降低.这种减少对于PCD更为明显，当使用200锥与5锥窗口时，PCD值平均降低27.8%（相比之下，在这些相同的窗口尺寸之间，CDC的密度仅降低3.5%）。虽然PCD和CDC位置没有发生在给定蒙太奇内的相同位置，PCD-CDC偏移与采样窗口大小之间没有显著关系(p=0.8919).CDC位置在采样窗口中的变化较小，在10个窗口大小47.56µm²（与PCD位置的844.10µm²相比，每个参与者的平均95%置信度椭圆面积，p<0.0001)。
■在不同的采样窗口大小下，CDC指标比PCD指标更稳定。了解密度值如何根据用于对锥体马赛克进行采样的方法而变化可以有助于比较不同研究中的锥体密度数据。
