A Complete Review of Automatic Detection, Segmentation, and Quantification of Neovascularization in Optical Coherence Tomography Angiography Images

Author:

Tun Yar Zar1ORCID,Aimmanee Pakinee1

Affiliation:

1. School of Information, Computer and Communication Technology (ICT), Sirindhorn International Institute of Technology (SIIT), Thammasat University, Muang, Pathum Thani 12000, Thailand

Abstract

Optical coherence tomography (OCT) is revolutionizing the way we assess eye complications such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). With its ability to provide layer-by-layer information on the retina, OCT enables the early detection of abnormalities emerging underneath the retinal surface. The latest advancement in this field, OCT angiography (OCTA), takes this to the next level by providing detailed vascular information without requiring dye injections. One of the most significant indicators of DR and AMD is neovascularization, the abnormal growth of unhealthy vessels. In this work, the techniques and algorithms used for the automatic detection, classification, and segmentation of neovascularization in OCTA images are explored. From image processing to machine learning and deep learning, works related to automated image analysis of neovascularization are summarized from different points of view. The problems and future work of each method are also discussed.

Funder

National Research Council of Thailand

Center of Excellence in Biomedical Engineering of Thammasat University

Publisher

MDPI AG

Subject

Clinical Biochemistry

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