Abstract
In recent years, the medical image analysis field has experienced remarkable growth. Advances in computational power have made it possible to create increasingly complex diagnostic support systems based on deep learning. In ophthalmology, optical coherence tomography (OCT) enables the capture of highly detailed images of the retinal morphology, being the reference technology for the analysis of relevant ocular structures. This paper proposes a new methodology for the automatic segmentation of the main retinal layers using OCT images. The system provides a useful tool that facilitates the clinical evaluation of key ocular structures, such as the choroid, vitreous humour or inner retinal layers, as potential computational biomarkers for the analysis of different neurodegenerative disorders, including multiple sclerosis and Alzheimer’s disease.
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Acknowledgements
This research was funded by Instituto de Salud Carlos III, Government of Spain, DTS18/00136 research project; Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018-095894-B-I00 research project; Ministerio de Ciencia e Innovación, Government of Spain through the research project with reference PID2019-108435RB-I00; Consellería de Cultura, Educación e Universidade, Xunta de Galicia, Grupos de Referencia Competitiva, grant ref. ED431C 2020/24 and postdoctoral grant ref. ED481B 2021/059; Axencia Galega de Innovación (GAIN), Xunta de Galicia, grant ref. IN845D 2020/38; CITIC, Centro de Investigación de Galicia ref. ED431G 2019/01, receives financial support from Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%).
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Budiño, A., Ramos, L., de Moura, J., Novo, J., Penedo, M.G., Ortega, M. (2022). Robust Deep Learning-Based Approach for Retinal Layer Segmentation in Optical Coherence Tomography Images. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2022. EUROCAST 2022. Lecture Notes in Computer Science, vol 13789. Springer, Cham. https://doi.org/10.1007/978-3-031-25312-6_50
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