Enhancing Pathological Detection and Monitoring in OCT Volumes with Limited Slices using Convolutional Neural Networks and 3D Visualization Techniques

  1. Emilio López Varela 1
  2. Noelia Barreira 1
  3. Nuria Olivier Pascual
  4. Manuel G. Penedo
  1. 1 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

Libro:
VI Congreso XoveTIC: impulsando el talento científico
  1. Manuel Lagos Rodríguez (ed. lit.)
  2. Álvaro Leitao Rodríguez (ed. lit.)
  3. Tirso Varela Rodeiro (ed. lit.)
  4. Javier Pereira Loureiro (coord.)
  5. Manuel Francisco González Penedo (coord.)

Editorial: Servizo de Publicacións ; Universidade da Coruña

Año de publicación: 2023

Congreso: XoveTIC (6. 2023. A Coruña)

Tipo: Aportación congreso

DOI: 10.17979/SPUDC.000024.23 DIALNET GOOGLE SCHOLAR lock_openRUC editor

Resumen

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

Fuente de los datos: Dialnet