López-Varela, EmilioBarreira, NoeliaOlivier Pascual, NuriaGarcía Ben, EmmaRubio Cid, SaraPenedo, Manuel2024-05-212024-05-212023López-Varela, E., Barreira, N., Pascual, N., Garcia Ben, E., Rubio Cid, S., & Penedo, M. G. (2023). Choroid segmentation in non-EDI OCT images of multiple sclerosis patients. In Proceedings of V XoveTIC Conference. XoveTIC, A. Leitao and L. Ramos (eds.) . Kalpa Publications in Computing, Vol. 14, pp. 10-13. . https://doi.org/10.29007/8q522515-1762http://hdl.handle.net/2183/36564[Abstract]: Optical coherence tomography (OCT) is a non-invasive diagnostic technique that can image ocular structures. Recently, this imaging technique has been used to diagnose and monitor patients with multiple sclerosis (MS), as several clinical studies have linked the development of MS to various changes in the eye. Among the different structures, one of the relevant biomarkers for MS analysis is the choroid. Systems such as Enhanced Depth Imaging (EDI) provide detailed images of the choroid region. However, OCT images are not routinely captured using this technology unless the study is specifically focused on choroidal analysis. In this work we propose a robust approach, based on convolutional neural networks to segment the choroid in non-EDI OCT images. The results obtained show that the proposed network manages to delimit the inferior contour of the choroid in a similar way to the experts.eng© 2023, the Authors.Choroid segmentationConvolutional neural networkDeep learningMultiple sclerosisOptical coherence tomographyconference outputopen access