Varpa Research Group
Published May 15, 2022 | Version v1
Conference proceeding Open

High/Low Quality Style Transfer for Mutual Conversion of OCT Images Using Contrastive Unpaired Translation Generative Adversarial Networks

Authors/Creators

  • 1. ROR icon Universidade da Coruña
  • 2. University of A Coruña

Abstract (English)

Recent advances in artificial intelligence and deep learning models are contributing to the development of advanced computer-aided diagnosis (CAD) systems. In the context of medical imaging, Optical Coherence Tomography (OCT) is a valuable technique that is able to provide cross-sectional visualisations of the ocular tissue. However, OCT is constrained by a limitation between the quality of the visualisations that it can produce and the overall amount of tissue that can be analysed at once. This limitation leads to a scarcity of high quality data, a problem that is very prevalent when developing machine learning-based CAD systems intended for medical imaging. To mitigate this problem, we present a novel methodology for the unpaired conversion of OCT images acquired with a low quality extensive scanning preset into the visual style of those taken with a high quality intensive scan and vice versa. This is achieved by employing contrastive unpaired translation generative adversarial networks to convert between the visual styles of the different acquisition presets. The results we obtained in the validation experiments show that these synthetic generated images can mirror the visual features of the original ones while preserving the natural tissue texture, effectively increasing the total number of available samples that can be used to train robust machine learning-based CAD systems.

Other (English)

This research was funded by Instituto de Salud Carlos III, Government of Spain, [DTS18/00136]; Ministerio de Ciencia e Innovación y Universidades, Government of Spain, [RTI2018-095894-B-I00]; Ministerio de Ciencia e Innovación, Government of Spain through the research project [PID2019-108435RB-I00]; Consellería de Cultura, Educación e Universidade, Xunta de Galicia, Grupos de Referencia Competitiva, [ED431C 2020/24], predoctoral grant [ED481A 2021/161]; Axencia Galega de Innovación (GAIN), Xunta de Galicia, [IN845D 2020/38]; CITIC, Centro de Investigación de Galicia [ED431G 2019/01], receives financial support from Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%).

Files

Gende_Mateo_2022_High_low_quality_style_transfer_OCT_Images.pdf

Files (1.9 MB)

Additional details

Identifiers

Handle
2183/36424