School of Optometry and Vision Science, University of Bradford, Bradford, UK.
Department of Ophthalmology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
Transl Vis Sci Technol. 2021 Oct 4;10(12):1. doi: 10.1167/tvst.10.12.1.
To introduce and evaluate the performance in detecting glaucomatous abnormalities of a novel method for extracting en face slab images (SMAS), which considers varying individual anatomy and configuration of retinal nerve fiber bundles.
Dense central retinal spectral domain optical coherence tomography scans were acquired in 16 participants with glaucoma and 19 age-similar controls. Slab images were generated by averaging reflectivity over different depths below the inner limiting membrane according to several methods. SMAS considered multiple 16 µm thick slabs from 8 to 116 µm below the inner limiting membrane, whereas 5 alternative methods considered single summary slabs of various thicknesses and depths. Superpixels in eyes with glaucoma were considered abnormal if below the first percentile of distributions fitted to control data for each method. The ability to detect glaucoma defects was measured by the proportion of abnormal superpixels. Proportion of superpixels below the fitted first percentile in controls was used as a surrogate false-positive rate. The effects of slab methods on performance measures were evaluated with linear mixed models.
The ability to detect glaucoma defects varied between slab methods, χ2(5) = 120.9, P < 0.0001, with SMAS showing proportion of abnormal superpixels 0.05 to 0.09 greater than alternatives (all P < 0.0001). No slab method found abnormal superpixels in controls.
SMAS outperformed alternatives in detecting abnormalities in eyes with glaucoma. SMAS evaluates all depths with potential retinal nerve fiber bundle presence by combining multiple slabs, resulting in greater detection of reflectance abnormalities with no increase in surrogate false positives.
SMAS may be used to objectively detect glaucoma defects in en face optical coherence tomography images.
介绍并评估一种新的提取视网膜神经纤维层(RNFL)图像的方法(SMAS)在检测青光眼异常方面的性能,该方法考虑了个体解剖结构和视网膜神经纤维束的不同配置。
对 16 名青光眼患者和 19 名年龄匹配的对照组进行密集的中央视网膜光谱域光学相干断层扫描。根据几种方法,在视网膜内界膜下方不同深度的反射率进行平均,生成板层图像。SMAS 考虑了从视网膜内界膜下方 8 到 116μm 的多个 16μm 厚的板层,而 5 种替代方法考虑了不同厚度和深度的单个综合板层。如果青光眼患者的超像素低于为每种方法的对照数据拟合的分布的第一个百分位数,则认为其异常。通过异常超像素的比例来衡量检测青光眼缺陷的能力。将对照组中低于拟合第一个百分位数的超像素比例用作替代假阳性率。使用线性混合模型评估板层方法对性能指标的影响。
不同板层方法检测青光眼缺陷的能力存在差异,χ2(5) = 120.9,P < 0.0001,SMAS 显示异常超像素的比例比其他方法高 0.05 到 0.09(均 P < 0.0001)。没有板层方法在对照组中发现异常超像素。
SMAS 在检测青光眼患者的异常方面优于其他替代方法。SMAS 通过组合多个板层评估所有潜在存在视网膜神经纤维束的深度,从而更有效地检测反射率异常,而不会增加替代假阳性。
SMAS 可能用于在光学相干断层扫描图像中客观地检测青光眼缺陷。