Impact of the Region of Analysis on the Performance of the Automatic Epiretinal Membrane Segmentation in OCT Images
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- Investigación (FIC) [1644]
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Impact of the Region of Analysis on the Performance of the Automatic Epiretinal Membrane Segmentation in OCT ImagesDate
2023-02-10Citation
Gende, M., Morís, D.I., de Moura, J., Novo, J., Ortega, M. (2022). Impact of the Region of Analysis on the Performance of the Automatic Epiretinal Membrane Segmentation in OCT 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_46
Abstract
[Absctract]: The Epiretinal Membrane (ERM) is an ocular pathology that can cause permanent visual loss if left untreated for long. Despite its transparency, it is possible to visualise the ERM in Optical Coherence Tomography (OCT) images. In this work, we present a study on the impact of the analysis region on the performance of an automatic ERM segmentation methodology using OCT images. For this purpose, we tested 5 different sliding windows sizes ranging from
to pixels to calibrate the impact of the field of view under analysis. Furthermore, 3 different approaches are proposed to enable the analysis of the regions close to the edges of the images. The proposed approaches provided satisfactory results, with each of them interacting differently with the variations in window size.
Keywords
Computer-aided diagnosis
Optical coherence tomography
Epiretinal membrane
Segmentation
Deep learning
Optical coherence tomography
Epiretinal membrane
Segmentation
Deep learning
Description
Eurocast 2022, 18th International Conference on Computer Aided Systems Theory. Museo Elder de la Ciencia y la Tecnología, Las Palmas de Gran Canaria, Spain, 20-25 February 2022.
Editor version
ISBN
978-3-031-25311-9 978-3-031-25312-6
