Retinal OCT Images: Graph-Based Layer Segmentation and Clinical Validation

Spectral-domain Optical Coherence Tomography (SD-OCT) is a critical tool in ophthalmology, providing high-resolution cross-sectional images of the retina. Accurate segmentation of sub-retinal layers is essential for diagnosing and monitoring retinal diseases. While manual segmentation by clinicians is the gold standard, it is subjective, time-intensive, and impractical for large-scale use. This study introduces an automated segmentation algorithm based on graph theory, utilizing a shortest-path graph-search technique to delineate seven intra-retinal boundaries. The algorithm incorporates a region of interest (ROI) selection to enhance efficiency, achieving a mean computation time of 0.93 s on standard systems suitable for real-time clinical applications. Image denoising was evaluated using Gaussian and wavelet-based filters. While wavelet-based denoising improved accuracy to some extent, its increased computation time (~10 s/image) was the trade-off. The intra-retinal layer thicknesses computed by the segmentation algorithm was consistent with previous studies and demonstrated high accuracy with respect to manual segmentation, thus indicating clinical relevance. Future research will explore integrating machine learning to improve robustness across diverse retinal pathologies, enhancing the algorithm’s applicability in clinical settings.