State-of-the-Art of Deep Learning in Multidisciplinary Optical Coherence Tomography Applications

被引：0
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作者：
Kalupahana, Deshan [1 ]
Kahatapitiya, Nipun Shantha [1 ]
Kamalathasan, Dilakshan [1 ]
Wijesinghe, Ruchire Eranga [2 ,3 ]
Silva, Bhagya Nathali [3 ,4 ]
Wijenayake, Udaya [1 ]
机构：
[1] University of Sri Jayewardenepura, Faculty of Engineering, Department of Computer Engineering, Nugegoda,10250, Sri Lanka
[2] Sri Lanka Institute of Information Technology, Faculty of Engineering, Department of Electrical and Electronic Engineering, Malabe,10115, Sri Lanka
[3] Sri Lanka Institute of Information Technology, Center for Excellence in Informatics, Electronics and Transmission (CIET), Malabe,10115, Sri Lanka
[4] Sri Lanka Institute of Information Technology, Faculty of Computing, Department of Information Technology, Malabe,10115, Sri Lanka
来源：
IEEE Access | 2024年 / 12卷
关键词：
Optical Coherence Tomography (OCT) emerged as a technology for the detection of retinal disease. Owing to its excellent performance and ability to provide in-vivo high-resolution images; the popularity increased dramatically across various application domains. Consequently; OCT has been widely used in other branches of medical applications; i.e; oncology and otolaryngology; industry; and agriculture. Despite its widespread use; OCT image analysis is an inherently subjective; laborious; and time-intensive task that requires expertise. Deep Learning (DL) stands as the current state-of-the-art method for image analysis. Hence; several research groups have directed their efforts toward incorporating DL algorithms with OCT imaging to reduce the time as well as the subjectivity. This article comprehensively reviews the principal technological advancements in DL methods applied across various OCT applications. Additionally; it explores the latest trends in developing DL methods for OCT; highlights their limitations; and discusses future opportunities in a comprehensive manner. © 2013 IEEE;
D O I：
10.1109/ACCESS.2024.3492389
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页码：164462 / 164490
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