Evaluating Descriptive Metrics of the Human Cone Mosaic.
Evaluating Descriptive Metrics of the Human Cone Mosaic.
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DOI：
10.1167/iovs.16-19072
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发表时间：
2016-06-01
影响因子：
4.4
通讯作者：
Carroll J
中科院分区：
文献类型：
--
作者：
Cooper RF;Wilk MA;Tarima S;Carroll J
评估用于描述锥体镶嵌的指标如何响应模拟光感受器欠采样（即细胞丢失或错误识别）而变化。使用自适应光学检眼镜，我们获得了 20 名健康受试者沿颞、上、下和鼻经线从固定中心到 10° 的锥形镶嵌图像。沿着每条经线定期提取感兴趣区域 (n = 1780)。使用各种指标评估锥体镶嵌几何形状 - 密度、密度恢复剖面距离 (DRPD)、最近邻距离 (NND)、单元间距离 (ICD)、最远邻距 (FND)、六面 Voronoi 单元的百分比、最近邻规则性 (NNR)、相邻规则数 (NoNR) 和 Voronoi 单元面积规则性 (VCAR)。通过确定获得 80% 统计功效所需的模拟损失水平来评估每个指标的“性能”。在评估的指标中，NND 和 DRPD 对欠采样最不敏感，将丢失 50% 坐标的马赛克分类为与正常马赛克无法区分。 NoNR 是最敏感的，检测到与正常值的显着偏差，仅损失 10% 的细胞。锥体间距度量的稳健性使得它们不适合可靠地检测与正常值的微小偏差或跟踪马赛克随时间的微小变化。相比之下，规律性指标对扩散损失更敏感，因此更适合检测此类变化，前提是错误识别的细胞比例最小。将指标与各种灵敏度相结合可以提供关于光感受器马赛克完整性的更完整的图片。
To evaluate how metrics used to describe the cone mosaic change in response to simulated photoreceptor undersampling (i.e., cell loss or misidentification). Using an adaptive optics ophthalmoscope, we acquired images of the cone mosaic from the center of fixation to 10° along the temporal, superior, inferior, and nasal meridians in 20 healthy subjects. Regions of interest (n = 1780) were extracted at regular intervals along each meridian. Cone mosaic geometry was assessed using a variety of metrics − density, density recovery profile distance (DRPD), nearest neighbor distance (NND), intercell distance (ICD), farthest neighbor distance (FND), percentage of six-sided Voronoi cells, nearest neighbor regularity (NNR), number of neighbors regularity (NoNR), and Voronoi cell area regularity (VCAR). The “performance” of each metric was evaluated by determining the level of simulated loss necessary to obtain 80% statistical power. Of the metrics assessed, NND and DRPD were the least sensitive to undersampling, classifying mosaics that lost 50% of their coordinates as indistinguishable from normal. The NoNR was the most sensitive, detecting a significant deviation from normal with only a 10% cell loss. The robustness of cone spacing metrics makes them unsuitable for reliably detecting small deviations from normal or for tracking small changes in the mosaic over time. In contrast, regularity metrics are more sensitive to diffuse loss and, therefore, better suited for detecting such changes, provided the fraction of misidentified cells is minimal. Combining metrics with a variety of sensitivities may provide a more complete picture of the integrity of the photoreceptor mosaic.