# Retinal OCT Images: Graph-Based Layer Segmentation and Clinical Validation +
## 1 Introduction  
Optical Coherence Tomography (OCT) is a fast, non-invasive medical imaging method that provides detailed cross-sectional images of the retina [1, 2]. This technology is essential in ophthalmology, enabling the early identification of eye diseases by examining the retina's microstructure. Properties of the ocular media such as its high spectral transmissibility and interferometric sensitivity are the confounding factors that make OCT a high-resolution imaging technique for the cross-section of the retina [3, 4, 5]. This capability makes OCT a critical tool in ophthalmology, allowing for detailed examination of the retina's microstructure and the early detection of pathological changes [6, 7, 8]. The early detection and monitoring of several ophthalmic and neurodegenerative pathologies, such as glaucoma, diabetic retinopathy (DR), age-related macular degeneration(AMD), and macular edema, require detailed investigation of the individual sub-retinal layers [6; 7; 8]. OCT images are segmented to precisely outline these layer boundaries, allowing for their measurement and visualization. This process is crucial for understanding how these diseases develop and respond to treatment. Despite its advantages, the increasing volume of volumetric imaging data presents significant challenges for manual segmentation. Manual segmentation is labor-intensive, highly subjective, and involves substantial time requirements and overhead costs, creating a significant bottleneck in clinical workflows [8; 9]. Additionally, the performances of the in-built segmentation algorithms of the OCT imaging devices exhibit inconsistencies between versions and across different manufacturer [10]. Furthermore, these algorithms are highly variable across image quality and often struggle with poor image contrast and heavy speckle noise, particularly when dealing with pathological retinas [10; 11]. These limitations underscore the pressing need for the development of fast, accurate, robust, and optimized automated segmentation algorithms [10; 11]. Such algorithms would enhance the clinical utility of OCT by providing consistent and reliable segmentation results, independent of operator expertise. Automated segmentation has the potential to revolutionize clinical practice by significantly reducing the time and effort required for image analysis, thereby improving workflow efficiency and diagnostic accuracy [12; 13]. The primary motivation for this study was to develop a robust segmentation algorithm to automate the process of intra-retinal layer segmentation in OCT images. One of the primary challenges to be tackled by automated segmentation algorithms is the impact of heavy speckle noise and low optical contrast in retinal OCT scans [8; 14; 15; 16; 17]. These factors complicate the accurate delineation of the retinal layers, necessitating the use of effective image denoising techniques. Hence, this study also aimed at exploring the impact of advanced denoising techniques on the accuracy of segmentation. Simple Gaussian filters, commonly used for denoising, can result in over-smoothing, which blurs the edges within images and adversely impacts boundary detection accuracy [18]. Advanced denoising techniques, such as those utilizing wavelet domains preceded by bilateral filtering, offer a more sophisticated approach by retaining boundary details while effectively reducing noise. We wanted to determine whether improvements in restoring the finer details of the boundaries within OCT images through wavelet-based denoising can enhance the overall efficiency and clinical performance of the automated segmentation by the algorithm, in comparison to traditional low-pass filter smoothing methods [19]. A validation study was also performed to assess efficacy of the segmentation results obtained from the automated graph-based segmentation algorithm. This study compared retinal layer thicknesses obtained from the automated segmentation approach with those derived from manual segmentation by expert clinicians.