Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/10460
Title: Unraveling the complexity of optical coherence tomography image segmentation using machine and deep learning techniques: A review
Authors: Dr. NAWAZ Mehmood 
Uvaliyev, Adilet 
Bibi, Khadija 
Wei, Hao 
Abaxi, Sai Mu Dalike 
Masood, Anum 
Shi, Peilun 
Ho, Ho-Pui 
Yuan, Wu 
Issue Date: 2023
Source: Computerized Medical Imaging and Graphics, 2023, vol. 108, article, no. 102269.
Journal: Computerized Medical Imaging and Graphics 
Abstract: Optical Coherence Tomography (OCT) is an emerging technology that provides three-dimensional images of the microanatomy of biological tissue in-vivo and at micrometer-scale resolution. OCT imaging has been widely used to diagnose and manage various medical diseases, such as macular degeneration, glaucoma, and coronary artery disease. Despite its wide range of applications, the segmentation of OCT images remains difficult due to the complexity of tissue structures and the presence of artifacts. In recent years, different approaches have been used for OCT image segmentation, such as intensity-based, region-based, and deep learning-based methods. This paper reviews the major advances in state-of-the-art OCT image segmentation techniques. It provides an overview of the advantages and limitations of each method and presents the most relevant research works related to OCT image segmentation. It also provides an overview of existing datasets and discusses potential clinical applications. Additionally, this review gives an in-depth analysis of machine learning and deep learning approaches for OCT image segmentation. It outlines challenges and opportunities for further research in this field.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/10460
ISSN: 1879-0771
0895-6111
DOI: https://doi.org/10.1016/j.compmedimag.2023.102269
Appears in Collections:Applied Data Science - Publication

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