![]() The Images data underlying the results presented in the study are available from ( ). This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All codes files are available from the Harvard database (DOI: ). Received: OctoAccepted: ApPublished: May 14, 2021Ĭopyright: © 2021 Sousa et al. (2021) Automatic segmentation of retinal layers in OCT images with intermediate age-related macular degeneration using U-Net and DexiNed. The results were promising, reaching an average absolute error of 0.49 pixel for ILM, 0.57 for RPE, and 0.66 for BM.Ĭitation: Sousa JA, Paiva A, Silva A, Almeida JD, Braz Junior G, Diniz JO, et al. The method uses two deep neural networks, U-Net and DexiNed to perform the segmentation. Thus, the objective of this work is to present a method for the segmentation of the inner limiting membrane (ILM), retinal pigment epithelium, and Bruch’s membrane in OCT images of healthy and Intermediate AMD patients. The use of CAD (Computer-Aided Detection) systems has contributed to increasing the chances of correct detection, assisting specialists in diagnosing and monitoring disease. Optical coherence tomography is one of the main exams for the detection and monitoring of AMD, which seeks changes through the evaluation of successive sectional cuts in the search for morphological changes caused by drusen. AMD is characterized by the presence of drusen, which causes changes in the physiological structure of the retinal pigment epithelium (RPE) and the boundaries of the Bruch’s membrane layer (BM). Age-related macular degeneration (AMD) is an eye disease that can cause visual impairment and affects the elderly over 50 years of age. ![]()
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