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

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...

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Bibliographic Details
Published in:IEEE access 2024, Vol.12, p.164462-164490
Main Authors: Kalupahana, Deshan, Kahatapitiya, Nipun Shantha, Kamalathasan, Dilakshan, Wijesinghe, Ruchire Eranga, Silva, Bhagya Nathali, Wijenayake, Udaya
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Classification
Deep learning
enhancement
generation
Image analysis
Image resolution
Imaging
Medical imaging
Ophthalmology
Optical Coherence Tomography
Optical imaging
Otolaryngology
Reinforcement learning
Retina
segmentation
State of the art
Unsupervised learning
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3492389