Cargando…
A new generative approach for optical coherence tomography data scarcity: unpaired mutual conversion between scanning presets
In optical coherence tomography (OCT), there is a trade-off between the scanning time and image quality, leading to a scarcity of high quality data. OCT platforms provide different scanning presets, producing visually distinct images, limiting their compatibility. In this work, a fully automatic met...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10083164/ https://www.ncbi.nlm.nih.gov/pubmed/36680707 http://dx.doi.org/10.1007/s11517-022-02742-6 |
_version_ | 1785021448561098752 |
---|---|
author | Gende, Mateo de Moura, Joaquim Novo, Jorge Penedo, Manuel G. Ortega, Marcos |
author_facet | Gende, Mateo de Moura, Joaquim Novo, Jorge Penedo, Manuel G. Ortega, Marcos |
author_sort | Gende, Mateo |
collection | PubMed |
description | In optical coherence tomography (OCT), there is a trade-off between the scanning time and image quality, leading to a scarcity of high quality data. OCT platforms provide different scanning presets, producing visually distinct images, limiting their compatibility. In this work, a fully automatic methodology for the unpaired visual conversion of the two most prevalent scanning presets is proposed. Using contrastive unpaired translation generative adversarial architectures, low quality images acquired with the faster Macular Cube preset can be converted to the visual style of high visibility Seven Lines scans and vice-versa. This modifies the visual appearance of the OCT images generated by each preset while preserving natural tissue structure. The quality of original and synthetic generated images was compared using brisque. The synthetic generated images achieved very similar scores to original images of their target preset. The generative models were validated in automatic and expert separability tests. These models demonstrated they were able to replicate the genuine look of the original images. This methodology has the potential to create multi-preset datasets with which to train robust computer-aided diagnosis systems by exposing them to the visual features of different presets they may encounter in real clinical scenarios without having to obtain additional data. [Figure: see text] |
format | Online Article Text |
id | pubmed-10083164 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-100831642023-04-11 A new generative approach for optical coherence tomography data scarcity: unpaired mutual conversion between scanning presets Gende, Mateo de Moura, Joaquim Novo, Jorge Penedo, Manuel G. Ortega, Marcos Med Biol Eng Comput Original Article In optical coherence tomography (OCT), there is a trade-off between the scanning time and image quality, leading to a scarcity of high quality data. OCT platforms provide different scanning presets, producing visually distinct images, limiting their compatibility. In this work, a fully automatic methodology for the unpaired visual conversion of the two most prevalent scanning presets is proposed. Using contrastive unpaired translation generative adversarial architectures, low quality images acquired with the faster Macular Cube preset can be converted to the visual style of high visibility Seven Lines scans and vice-versa. This modifies the visual appearance of the OCT images generated by each preset while preserving natural tissue structure. The quality of original and synthetic generated images was compared using brisque. The synthetic generated images achieved very similar scores to original images of their target preset. The generative models were validated in automatic and expert separability tests. These models demonstrated they were able to replicate the genuine look of the original images. This methodology has the potential to create multi-preset datasets with which to train robust computer-aided diagnosis systems by exposing them to the visual features of different presets they may encounter in real clinical scenarios without having to obtain additional data. [Figure: see text] Springer Berlin Heidelberg 2023-01-21 2023 /pmc/articles/PMC10083164/ /pubmed/36680707 http://dx.doi.org/10.1007/s11517-022-02742-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Gende, Mateo de Moura, Joaquim Novo, Jorge Penedo, Manuel G. Ortega, Marcos A new generative approach for optical coherence tomography data scarcity: unpaired mutual conversion between scanning presets |
title | A new generative approach for optical coherence tomography data scarcity: unpaired mutual conversion between scanning presets |
title_full | A new generative approach for optical coherence tomography data scarcity: unpaired mutual conversion between scanning presets |
title_fullStr | A new generative approach for optical coherence tomography data scarcity: unpaired mutual conversion between scanning presets |
title_full_unstemmed | A new generative approach for optical coherence tomography data scarcity: unpaired mutual conversion between scanning presets |
title_short | A new generative approach for optical coherence tomography data scarcity: unpaired mutual conversion between scanning presets |
title_sort | new generative approach for optical coherence tomography data scarcity: unpaired mutual conversion between scanning presets |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10083164/ https://www.ncbi.nlm.nih.gov/pubmed/36680707 http://dx.doi.org/10.1007/s11517-022-02742-6 |
work_keys_str_mv | AT gendemateo anewgenerativeapproachforopticalcoherencetomographydatascarcityunpairedmutualconversionbetweenscanningpresets AT demourajoaquim anewgenerativeapproachforopticalcoherencetomographydatascarcityunpairedmutualconversionbetweenscanningpresets AT novojorge anewgenerativeapproachforopticalcoherencetomographydatascarcityunpairedmutualconversionbetweenscanningpresets AT penedomanuelg anewgenerativeapproachforopticalcoherencetomographydatascarcityunpairedmutualconversionbetweenscanningpresets AT ortegamarcos anewgenerativeapproachforopticalcoherencetomographydatascarcityunpairedmutualconversionbetweenscanningpresets AT gendemateo newgenerativeapproachforopticalcoherencetomographydatascarcityunpairedmutualconversionbetweenscanningpresets AT demourajoaquim newgenerativeapproachforopticalcoherencetomographydatascarcityunpairedmutualconversionbetweenscanningpresets AT novojorge newgenerativeapproachforopticalcoherencetomographydatascarcityunpairedmutualconversionbetweenscanningpresets AT penedomanuelg newgenerativeapproachforopticalcoherencetomographydatascarcityunpairedmutualconversionbetweenscanningpresets AT ortegamarcos newgenerativeapproachforopticalcoherencetomographydatascarcityunpairedmutualconversionbetweenscanningpresets |