Guru Pradeep Reddy, T.Ashritha, K.S.Prajwala, T.M.Girish, G.N.Kothari, A.R.Koolagudi, S.G.Rajan, J.2026-02-062020Advances in Intelligent Systems and Computing, 2020, Vol.1022 AISC, , p. 279-29221945357https://doi.org/10.1007/978-981-32-9088-4_24https://idr.nitk.ac.in/handle/123456789/30803Visualization and analysis of Spectral Domain Optical Coherence Tomography (SD-OCT) cross-sectional scans has gained a lot of importance in the diagnosis of several retinal abnormalities. Quantitative analytic techniques like retinal thickness and volumetric analysis are performed on cross-sectional images of the retina for early diagnosis and prognosis of retinal diseases. However, segmentation of retinal layers from OCT images is a complicated task on account of certain factors like speckle noise, low image contrast and low signal-to-noise ratio amongst many others. Owing to the importance of retinal layer segmentation in diagnosing ophthalmic diseases, manual segmentation techniques have been proposed and adopted in clinical practice. Nonetheless, manual segmentations suffer from erroneous boundary detection issues. This paper thus proposes a fully automated semantic segmentation technique that uses an encoder–decoder architecture to accurately segment the prominent retinal layers. © 2020, Springer Nature Singapore Pte Ltd.Deep learningDilated convolutionsOptical coherence tomographyRetinaRetinal layer segmentation