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Addressing inter-device variations in optical coherence tomography angiography: will image-to-image translation systems help?

BACKGROUND: Optical coherence tomography angiography (OCTA) is an innovative technology providing visual and quantitative data on retinal microvasculature in a non-invasive manner. MAIN BODY: Due to variations in the technical specifications of different OCTA devices, there are significant inter-dev...

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Autores principales: Nouri, Hosein, Nasri, Reza, Abtahi, Seyed-Hossein
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10466880/
https://www.ncbi.nlm.nih.gov/pubmed/37644613
http://dx.doi.org/10.1186/s40942-023-00491-8
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author Nouri, Hosein
Nasri, Reza
Abtahi, Seyed-Hossein
author_facet Nouri, Hosein
Nasri, Reza
Abtahi, Seyed-Hossein
author_sort Nouri, Hosein
collection PubMed
description BACKGROUND: Optical coherence tomography angiography (OCTA) is an innovative technology providing visual and quantitative data on retinal microvasculature in a non-invasive manner. MAIN BODY: Due to variations in the technical specifications of different OCTA devices, there are significant inter-device differences in OCTA data, which can limit their comparability and generalizability. These variations can also result in a domain shift problem that may interfere with applicability of machine learning models on data obtained from different OCTA machines. One possible approach to address this issue may be unsupervised deep image-to-image translation leveraging systems such as Cycle-Consistent Generative Adversarial Networks (Cycle-GANs) and Denoising Diffusion Probabilistic Models (DDPMs). Through training on unpaired images from different device domains, Cycle-GANs and DDPMs may enable cross-domain translation of images. They have been successfully applied in various medical imaging tasks, including segmentation, denoising, and cross-modality image-to-image translation. In this commentary, we briefly describe how Cycle-GANs and DDPMs operate, and review the recent experiments with these models on medical and ocular imaging data. We then discuss the benefits of applying such techniques for inter-device translation of OCTA data and the potential challenges ahead. CONCLUSION: Retinal imaging technologies and deep learning-based domain adaptation techniques are rapidly evolving. We suggest exploring the potential of image-to-image translation methods in improving the comparability of OCTA data from different centers or devices. This may facilitate more efficient analysis of heterogeneous data and broader applicability of machine learning models trained on limited datasets in this field.
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spelling pubmed-104668802023-08-31 Addressing inter-device variations in optical coherence tomography angiography: will image-to-image translation systems help? Nouri, Hosein Nasri, Reza Abtahi, Seyed-Hossein Int J Retina Vitreous Commentary BACKGROUND: Optical coherence tomography angiography (OCTA) is an innovative technology providing visual and quantitative data on retinal microvasculature in a non-invasive manner. MAIN BODY: Due to variations in the technical specifications of different OCTA devices, there are significant inter-device differences in OCTA data, which can limit their comparability and generalizability. These variations can also result in a domain shift problem that may interfere with applicability of machine learning models on data obtained from different OCTA machines. One possible approach to address this issue may be unsupervised deep image-to-image translation leveraging systems such as Cycle-Consistent Generative Adversarial Networks (Cycle-GANs) and Denoising Diffusion Probabilistic Models (DDPMs). Through training on unpaired images from different device domains, Cycle-GANs and DDPMs may enable cross-domain translation of images. They have been successfully applied in various medical imaging tasks, including segmentation, denoising, and cross-modality image-to-image translation. In this commentary, we briefly describe how Cycle-GANs and DDPMs operate, and review the recent experiments with these models on medical and ocular imaging data. We then discuss the benefits of applying such techniques for inter-device translation of OCTA data and the potential challenges ahead. CONCLUSION: Retinal imaging technologies and deep learning-based domain adaptation techniques are rapidly evolving. We suggest exploring the potential of image-to-image translation methods in improving the comparability of OCTA data from different centers or devices. This may facilitate more efficient analysis of heterogeneous data and broader applicability of machine learning models trained on limited datasets in this field. BioMed Central 2023-08-29 /pmc/articles/PMC10466880/ /pubmed/37644613 http://dx.doi.org/10.1186/s40942-023-00491-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Commentary
Nouri, Hosein
Nasri, Reza
Abtahi, Seyed-Hossein
Addressing inter-device variations in optical coherence tomography angiography: will image-to-image translation systems help?
title Addressing inter-device variations in optical coherence tomography angiography: will image-to-image translation systems help?
title_full Addressing inter-device variations in optical coherence tomography angiography: will image-to-image translation systems help?
title_fullStr Addressing inter-device variations in optical coherence tomography angiography: will image-to-image translation systems help?
title_full_unstemmed Addressing inter-device variations in optical coherence tomography angiography: will image-to-image translation systems help?
title_short Addressing inter-device variations in optical coherence tomography angiography: will image-to-image translation systems help?
title_sort addressing inter-device variations in optical coherence tomography angiography: will image-to-image translation systems help?
topic Commentary
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10466880/
https://www.ncbi.nlm.nih.gov/pubmed/37644613
http://dx.doi.org/10.1186/s40942-023-00491-8
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