Retinal OCT image segmentation with deep learning: A review of advances, datasets, and evaluation metrics

Optical coherence tomography (OCT) is a widely used imaging technology in ophthalmic clinical practice, providing non-invasive access to high-resolution retinal images. Segmentation of anatomical structures and pathological lesions in retinal OCT images, directly impacts clinical decisions. While commercial OCT devices segment multiple retinal layers in healthy eyes, their performance degrades severely under pathological conditions. In recent years, the rapid advancements in deep learning have significantly driven research in OCT image segmentation. This review provides a comprehensive overview of the latest developments in deep learning-based segmentation methods for retinal OCT images. Additionally, it summarizes the medical significance, publicly available datasets, and commonly used evaluation metrics in this field. The review also discusses the current challenges faced by the research community and highlights potential future directions.