Enhancing Pathological Detection and Monitoring in OCT Volumes with Limited Slices using Convolutional Neural Networks and 3D Visualization Techniques
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Universidade da Coruña
info
- Manuel Lagos Rodríguez (ed. lit.)
- Álvaro Leitao Rodríguez (ed. lit.)
- Tirso Varela Rodeiro (ed. lit.)
- Javier Pereira Loureiro (coord.)
- Manuel Francisco González Penedo (coord.)
Editorial: Servizo de Publicacións ; Universidade da Coruña
Ano de publicación: 2023
Congreso: XoveTIC (6. 2023. A Coruña)
Tipo: Achega congreso
Resumo
Optical Coherence Tomography (OCT) is a non-invasive imaging technique with a crucial role in the monitoring of a wide range of diseases. In order to make a good diagnosis it is essential that clinicians can observe any subtle changes that appear in the multiple ocular structures, so it is imperative that the 3D OCT volumes have good resolution in each axis. Unfortunately, there is a trade-off between image quality and the number of volume slices. In this work, we use a convolutional neural network to generate the intermediate synthetic slices of the OTC volumes and we propose a few variants of a 3D reconstruction algorithm to create visualizations that emphasize the changes present in multiple retinal structures to aid clinicians in the diagnostic process