Camara José, Rezende Roberto, Pires Ivan Miguel, Cunha António
Departamento de Ciências e Tecnologia, Universidade Aberta, 1250-100 Lisboa, Portugal.
Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal.
J Clin Med. 2022 Jul 2;11(13):3850. doi: 10.3390/jcm11133850.
Public databases for glaucoma studies contain color images of the retina, emphasizing the optic papilla. These databases are intended for research and standardized automated methodologies such as those using deep learning techniques. These techniques are used to solve complex problems in medical imaging, particularly in the automated screening of glaucomatous disease. The development of deep learning techniques has demonstrated potential for implementing protocols for large-scale glaucoma screening in the population, eliminating possible diagnostic doubts among specialists, and benefiting early treatment to delay the onset of blindness. However, the images are obtained by different cameras, in distinct locations, and from various population groups and are centered on multiple parts of the retina. We can also cite the small number of data, the lack of segmentation of the optic papillae, and the excavation. This work is intended to offer contributions to the structure and presentation of public databases used in the automated screening of glaucomatous papillae, adding relevant information from a medical point of view. The gold standard public databases present images with segmentations of the disc and cupping made by experts and division between training and test groups, serving as a reference for use in deep learning architectures. However, the data offered are not interchangeable. The quality and presentation of images are heterogeneous. Moreover, the databases use different criteria for binary classification with and without glaucoma, do not offer simultaneous pictures of the two eyes, and do not contain elements for early diagnosis.
青光眼研究的公共数据库包含视网膜的彩色图像，重点是视乳头。这些数据库旨在用于研究以及标准化的自动化方法，例如那些使用深度学习技术的方法。这些技术用于解决医学成像中的复杂问题，特别是在青光眼疾病的自动筛查中。深度学习技术的发展已显示出在人群中实施大规模青光眼筛查方案、消除专家之间可能存在的诊断疑虑以及有利于早期治疗以延缓失明发生方面的潜力。然而，这些图像是由不同的相机在不同的地点、从不同的人群组获取的，并且以视网膜的多个部位为中心。我们还可以提到数据量少、视乳头缺乏分割以及凹陷情况。这项工作旨在为青光眼视乳头自动筛查中使用的公共数据库的结构和呈现做出贡献，从医学角度添加相关信息。金标准公共数据库呈现的图像带有专家进行的视盘和杯状凹陷分割以及训练组和测试组之间的划分，可作为深度学习架构中使用的参考。然而，所提供的数据不可互换。图像的质量和呈现方式是异质的。此外，这些数据库在有青光眼和无青光眼的二元分类中使用不同的标准，不提供双眼的同步图像，并且不包含早期诊断的要素。
