Self-Supervised Deep-Learning Segmentation of Corneal Endothelium Specular Microscopy Images

Sergio Sanchez, Kevin Mendoza, Fernando Quintero, Angelica M. Prada, Alejandro Tello, Virgilio Galvis, Lenny A. Romero, Andres G. Marrugo

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Resumen

Computerized medical evaluation of the corneal endothelium is challenging because it requires costly equipment and specialized personnel, not to mention that conventional techniques require manual annotations that are difficult to acquire. This study aims to obtain reliable segmentations without requiring large data sets labeled by expert personnel. To address this problem, we use the Barlow Twins approach to pre-train the encoder of a UNet model in an unsupervised manner. Then, with few labeled data, we train the segmentation. Encouraging results show that it is possible to address the challenge of limited data availability using self-supervised learning. This model achieved a precision of 86%, obtaining a satisfactory performance. Using many images to learn good representations and a few labeled images to learn the semantic segmentation task is feasible. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Idioma originalInglés
Título de la publicación alojada2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Proceedings
EditoresAlvaro David Orjuela-Canon
EditorialInstitute of Electrical and Electronics Engineers Inc.
Volumen1865
ISBN (versión digital)9798350316599
DOI
EstadoPublicada - 18 nov. 2023
Evento2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Bogota, Colombia
Duración: 26 jul. 202328 jul. 2023

Serie de la publicación

Nombre2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Proceedings

Conferencia

Conferencia2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023
País/TerritorioColombia
CiudadBogota
Período26/07/2328/07/23

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