Tun Yar Zar, Aimmanee Pakinee
School of Information, Computer and Communication Technology (ICT), Sirindhorn International Institute of Technology (SIIT), Thammasat University, Muang, Pathum Thani 12000, Thailand.
Diagnostics (Basel). 2023 Nov 9;13(22):3407. doi: 10.3390/diagnostics13223407.
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.
光学相干断层扫描（OCT）正在彻底改变我们评估眼部并发症的方式，如糖尿病性视网膜病变（DR）和年龄相关性黄斑变性（AMD）。凭借其提供视网膜逐层信息的能力，OCT能够早期检测视网膜表面下出现的异常情况。该领域的最新进展——光学相干断层扫描血管造影（OCTA），通过无需注射染料就能提供详细的血管信息，将这一技术提升到了新的高度。DR和AMD最重要的指标之一是新生血管形成，即不健康血管的异常生长。在这项工作中，我们探索了用于OCTA图像中新生血管自动检测、分类和分割的技术与算法。从图像处理到机器学习和深度学习，从不同角度总结了与新生血管自动图像分析相关的工作。还讨论了每种方法存在的问题和未来的工作。
