# Retinal OCT Images: Graph-Based Layer Segmentation and Clinical Validation +
## 5 Conclusions  
The graph-based automated segmentation algorithm developed in this study demonstrates high-speed, accurate delineation of seven intra-retinal boundaries, segmenting six retinal layers in SD-OCT images. The comparative analysis of denoising techniques highlights the balance between accuracy and computational efficiency, with Gaussian filtering emerging as a practical option for clinical use despite the slight accuracy improvement offered by wavelet-based denoising. The algorithm significantly reduces the time and costs associated with manual segmentation of OCT images, making it a valuable tool for routine clinical practice. Its ability to accurately compute layer thicknesses enables the detection of retinal pathologies, potentially improving patient outcomes through timely diagnosis and monitoring. Future modifications could further enhance the algorithm's applicability to images with pathological features, broadening its utility in ophthalmic diagnostics. Overall, the validated automated graph-based segmentation algorithm represents a robust, efficient solution for the segmentation of intra-retinal layers in SD-OCT images, paving the way for its integration into clinical workflows and research settings. Future research should focus on refining the algorithm to enhance its performance in pathological cases, where retinal layers may be disrupted or altered due to disease. Integrating machine learning techniques could improve the algorithm's adaptability and accuracy, particularly in complex cases. Additionally, expanding the dataset to include diverse populations and a broader range of retinal conditions will help validate the algorithm's robustness and generalizability. Collaboration with clinical practitioners will be essential to ensure that the algorithm meets the practical needs of ophthalmologists and contributes to improved patient care. Conceptualization, V.L. and P.R. ; methodology, P.R. ; software, P.R. ; validation, M.K.P. ; formal analysis, P.R. ; investigation, P.R. ; resources, V.L. ; writing--original draft preparation, P.R. ; writing--review and editing, V.L. ; visualization, P.R. ; supervision, V.L. ; project administration, V.L. ; funding acquisition, V.L. All authors have read and agreed to the published version of the manuscript. This research was funded by a NSERC Discovery Grant (V.L.). Ethical review and approval were waived for this study since de-identified retinal OCT images used in this research were acquired externally from the Medical Research Foundation, Sankara Nethralaya, Chennai, India. The authors and researchers did not have access to any of the clinical data or identifiers associated with the dataset. Not applicable. An open-source dataset was constructed and previously published by the authors [31], which also includes the subset of SD-OCT images from normal healthy retina that were used for this study and is now publicly available. The authors would like to acknowledge Medical Research Foundation, Sankara Nethralaya, Chennai, India, for sharing the retinal SD-OCT images, and specially mention the contributions on [PERSON] for helping with the data curation. The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. The following abbreviations are used in this manuscript: \begin{tabular}{l l} OCT & Optical Coherence Tomography \\ SD-OCT & Spectral Domain Optical Coherence Tomography \\ DR & Diabetic Retinopathy \\ AMD & Age-related Macular Degeneration \\ ROI & Region of Interest \\ BPI & Boundary Point Indices \\ GUI & Graphical User Interface \\ ILM & Internal Limiting Membrane \\ RNFL & Retinal Nerve Fiber Layer \\ GCL & Ganglion Cell Layer \\ IPL & Inner Plexiform Layer \\ INL & Inner Nuclear Layer \\ OPL & Outer Plexiform Layer \\ ONL & Outer Nuclear Layer \\ IS & Inner Segment \\ OS & Outer Segment \\ RPE & Retinal Pigment Epithelium \\ RAM & Random Access Memory \\ \end{tabular}