Annunziata Roberto, Kheirkhah Ahmad, Aggarwal Shruti, Cavalcanti Bernardo M, Hamrah Pedram, Trucco Emanuele
Computer Vision and Image Processing Group School of Science and Engineering (Computing), University of Dundee, Dundee, United Kingdom.
Ocular Surface Imaging Center and Cornea and Refractive Surgery Service, Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts, United States.
Invest Ophthalmol Vis Sci. 2016 Mar;57(3):1132-9. doi: 10.1167/iovs.15-18513.
To assess the performance of a novel system for automated tortuosity estimation and interpretation.
A supervised strategy (driven by observers' grading) was employed to automatically identify the combination of tortuosity measures (i.e., tortuosity representation) leading to the best agreement with the observers. We investigated 18 tortuosity measures including curvature and density of inflection points, computed at multiple spatial scales. To leverage tortuosity interpretation, we propose the tortuosity plane (TP) onto which each image is mapped. Experiments were carried out on 140 images of subbasal nerve plexus of the central cornea, covering four levels of tortuosity. Three experienced observers graded each image independently.
The best tortuosity representation was the combination of mean curvature at spatial scales 2 and 5. These tortuosity measures were the axes of the proposed TP (interpretation). The system for tortuosity estimation revealed strong agreement with the observers on a global and per-level basis. The agreement with each observer (Spearman's correlation) was statistically significant (αs = 0.05, P < 0.0001) and higher than that of at least one of the other observers in two out of three cases (ρOUR = 0.7594 versus ρObs3 = 0.7225; ρOUR = 0.8880 versus ρObs1 = 0.8017, ρObs3 = 0.7315). Based on paired-sample t-tests, these improvements were significant (P < 0.001).
Our automated system stratifies images by four tortuosity levels (discrete scale) matching or exceeding the accuracy of experienced observers. Of importance, the TP allows the assessment of tortuosity on a two-dimensional continuous scale, thus leading to a finer discrimination among images.
评估一种用于自动迂曲度估计与解读的新型系统的性能。
采用一种监督策略（由观察者评分驱动）来自动识别与观察者达成最佳一致性的迂曲度测量指标组合（即迂曲度表示）。我们研究了18种迂曲度测量指标，包括在多个空间尺度上计算的曲率和拐点密度。为了利用迂曲度解读，我们提出了迂曲度平面（TP），每个图像都映射到该平面上。对140张中央角膜基底神经丛图像进行了实验，涵盖四个迂曲度水平。三名经验丰富的观察者分别对每张图像进行评分。
最佳的迂曲度表示是空间尺度2和5处的平均曲率组合。这些迂曲度测量指标是所提出的TP（解读）的轴。迂曲度估计系统在整体和每个水平上都与观察者表现出高度一致性。与每位观察者的一致性（斯皮尔曼相关性）具有统计学意义（α = 0.05，P < 0.0001），并且在三分之二的情况下高于其他至少一位观察者的一致性（ρOUR = 0.7594对ρObs3 = 0.7225；ρOUR = 0.8880对ρObs1 = 0.8017，ρObs3 = 0.7315）。基于配对样本t检验，这些改进具有显著性（P < 0.001）。
我们的自动化系统通过四个迂曲度水平（离散尺度）对图像进行分层，其准确性与经验丰富的观察者相当或更高。重要的是，TP允许在二维连续尺度上评估迂曲度，从而在图像之间实现更精细的区分。
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