Yeh Ryan Y, Nischal Ken K, LeDuc Philip, Cagan Jonathan
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
Division of Pediatric Ophthalmology, Strabismus and Adult Motility, University of Pittsburgh Medical Center Children's Hospital, Pittsburgh, PA, USA.
Transl Vis Sci Technol. 2020 Aug 24;9(9):36. doi: 10.1167/tvst.9.9.36. eCollection 2020 Aug.
Blood vessel networks within the retina are crucial for maintaining tissue perfusion and therefore good vision. Their complexity and unique patterns often require a steep learning curve for humans to identify trends and changes in the shape and topology of the networks, even though there exists much information important to identifying disease within them.
Through image processing, the vasculature is isolated from other features of the fundus images, forcing the viewer to focus on the complex vascular feature. This article explores an approach using a grammar based on shape to describe retinal vasculature and to generate realistic and increasingly unrealistic artificial vascular networks that are then reviewed by ophthalmologists via digital survey. The ophthalmologists are asked whether these artificial vascular networks appeared realistic or unrealistic.
With only three rules (initiate, branch, and curve), the grammar accomplishes these goals. Networks are generated by adding noise to rule parameters present in existing networks. Via the survey of synthetic networks generated with different noise parameters, a correlation between noise in the branch rule and realistic association is revealed.
By creating a language to describe retinal vasculature, this article allows for the potential of new insight into such an important but less understood feature of the retina, which in the future may play a role in diagnosing or helping to predict types of ocular disease.
Applying shape grammar to describe retinal vasculature permits new understanding, which in turn provides the potential for new diagnostic tools.
视网膜内的血管网络对于维持组织灌注从而保持良好视力至关重要。尽管血管网络中存在许多对疾病识别很重要的信息，但它们的复杂性和独特模式往往需要人类经过陡峭的学习曲线才能识别网络形状和拓扑结构的趋势及变化。
通过图像处理，将脉管系统与眼底图像的其他特征隔离开来，促使观察者专注于复杂的血管特征。本文探索了一种基于形状的语法来描述视网膜脉管系统并生成逼真及越来越不逼真的人工血管网络的方法，然后眼科医生通过数字调查对这些网络进行评估。眼科医生被问及这些人工血管网络看起来是逼真还是不逼真。
仅用三条规则（起始、分支和弯曲），该语法就实现了这些目标。通过向现有网络中存在的规则参数添加噪声来生成网络。通过对用不同噪声参数生成的合成网络进行调查，揭示了分支规则中的噪声与逼真程度之间的相关性。
通过创建一种描述视网膜脉管系统的语言，本文为深入了解视网膜这一重要但了解较少的特征提供了新的可能性，未来这可能在眼部疾病的诊断或帮助预测疾病类型方面发挥作用。
应用形状语法来描述视网膜脉管系统可带来新的理解，进而为新的诊断工具提供了潜力。
