Clinical Ophthalmology (Mar 2022)
Machine Learning and Deep Learning Techniques for Optic Disc and Cup Segmentation – A Review
Abstract
Mohammed Alawad,1 Abdulrhman Aljouie,1 Suhailah Alamri,2,3 Mansour Alghamdi,4 Balsam Alabdulkader,4 Norah Alkanhal,2 Ahmed Almazroa2 1Department of Biostatistics and Bioinformatics, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia; 2Department of Imaging Research, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for health Sciences, Riyadh, Saudi Arabia; 3Research Labs, National Center for Artificial Intelligence, Riyadh, Saudi Arabia; 4Department of Optometry and Vision Sciences College of Applied Medical Sciences, King Saud University, Riyadh, Saudi ArabiaCorrespondence: Ahmed Almazroa; Abdulrhman Aljouie, Email [email protected]; [email protected]: 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.Methods: 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.Results: 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.Conclusion: 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.Keywords: glaucoma, glaucoma screening, fundus images, big images data
