Alawad Mohammed, Aljouie Abdulrhman, Alamri Suhailah, Alghamdi Mansour, Alabdulkader Balsam, Alkanhal Norah, Almazroa Ahmed
Department of Biostatistics and Bioinformatics, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.
Department of Imaging Research, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for health Sciences, Riyadh, Saudi Arabia.
Clin Ophthalmol. 2022 Mar 11;16:747-764. doi: 10.2147/OPTH.S348479. eCollection 2022.
Globally, glaucoma is the second leading cause of blindness. Detecting glaucoma in the early stages is essential to avoid disease complications, which lead to blindness. Thus, computer-aided diagnosis systems are powerful tools to overcome the shortage of glaucoma screening programs.
A systematic search of public databases, including PubMed, Google Scholar, and other sources, was performed to identify relevant studies to overview the publicly available fundus image datasets used to train, validate, and test machine learning and deep learning methods. Additionally, existing machine learning and deep learning methods for optic cup and disc segmentation were surveyed and critically reviewed.
Eight fundus images datasets were publicly available with 15,445 images labeled with glaucoma or non-glaucoma, and manually annotated optic disc and cup boundaries were found. Five metrics were identified for evaluating the developed models. Finally, three main deep learning architectural designs were commonly used for optic disc and optic cup segmentation.
We provided future research directions to formulate robust optic cup and disc segmentation systems. Deep learning can be utilized in clinical settings for this task. However, many challenges need to be addressed before using this strategy in clinical trials. Finally, two deep learning architectural designs have been widely adopted, such as U-net and its variants.
在全球范围内，青光眼是导致失明的第二大主要原因。早期检测青光眼对于避免导致失明的疾病并发症至关重要。因此，计算机辅助诊断系统是克服青光眼筛查项目不足的有力工具。
对包括PubMed、谷歌学术等在内的公共数据库进行系统检索，以识别相关研究，从而概述用于训练、验证和测试机器学习及深度学习方法的公开可用眼底图像数据集。此外，对现有的用于视杯和视盘分割的机器学习和深度学习方法进行了调查和批判性审查。
有八个眼底图像数据集可供公开使用，其中15445张图像标注了青光眼或非青光眼情况，并且发现了手动标注的视盘和视杯边界。确定了五个用于评估所开发模型的指标。最后，三种主要的深度学习架构设计常用于视盘和视杯分割。
我们提供了未来的研究方向，以制定强大的视杯和视盘分割系统。深度学习可用于临床环境中的这项任务。然而，在临床试验中使用该策略之前，还需要解决许多挑战。最后，两种深度学习架构设计已被广泛采用，如U-net及其变体。
