Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China.
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway.
Comput Med Imaging Graph. 2023 Sep;108:102269. doi: 10.1016/j.compmedimag.2023.102269. Epub 2023 Jul 14.
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.
光学相干断层扫描（OCT）是一种新兴技术，可提供生物组织微观结构的三维图像，并具有微米级分辨率。OCT 成像已广泛用于诊断和管理各种医学疾病，如黄斑变性、青光眼和冠状动脉疾病。尽管 OCT 成像具有广泛的应用，但由于组织结构的复杂性和伪影的存在，OCT 图像的分割仍然很困难。近年来，已经使用了不同的方法来进行 OCT 图像分割，例如基于强度、基于区域和基于深度学习的方法。本文综述了 OCT 图像分割技术的主要进展。它概述了每种方法的优缺点，并介绍了与 OCT 图像分割相关的最相关研究工作。它还概述了现有的数据集，并讨论了潜在的临床应用。此外，本综述还对 OCT 图像分割的机器学习和深度学习方法进行了深入分析。它概述了该领域进一步研究的挑战和机遇。
