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SSN2V: unsupervised OCT denoising using speckle split
Denoising in optical coherence tomography (OCT) is important to compensate the low signal-to-noise ratio originating from laser speckle. In recent years learning algorithms have been established as the most powerful denoising approach. Especially unsupervised denoising is an interesting topic since...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10299996/ https://www.ncbi.nlm.nih.gov/pubmed/37369731 http://dx.doi.org/10.1038/s41598-023-37324-5 |
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author | Schottenhamml, Julia Würfl, Tobias Ploner, Stefan B. Husvogt, Lennart Hohberger, Bettina Fujimoto, James G. Maier, Andreas |
author_facet | Schottenhamml, Julia Würfl, Tobias Ploner, Stefan B. Husvogt, Lennart Hohberger, Bettina Fujimoto, James G. Maier, Andreas |
author_sort | Schottenhamml, Julia |
collection | PubMed |
description | Denoising in optical coherence tomography (OCT) is important to compensate the low signal-to-noise ratio originating from laser speckle. In recent years learning algorithms have been established as the most powerful denoising approach. Especially unsupervised denoising is an interesting topic since it is not possible to acquire noise free scans with OCT. However, speckle in in-vivo OCT images contains not only noise but also information about blood flow. Existing OCT denoising algorithms treat all speckle equally and do not distinguish between the noise component and the flow information component of speckle. Consequently they either tend to either remove all speckle or denoise insufficiently. Unsupervised denoising methods tend to remove all speckle but create results that have a blurry impression which is not desired in a clinical application. To this end we propose the concept, that an OCT denoising method should, besides reducing uninformative noise, additionally preserve the flow-related speckle information. In this work, we present a fully unsupervised algorithm for single-frame OCT denoising (SSN2V) that fulfills these goals by incorporating known operators into our network. This additional constraint greatly improves the denoising capability compared to a network without. Quantitative and qualitative results show that the proposed method can effectively reduce the speckle noise in OCT B-scans of the human retina while maintaining a sharp impression outperforming the compared methods. |
format | Online Article Text |
id | pubmed-10299996 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-102999962023-06-29 SSN2V: unsupervised OCT denoising using speckle split Schottenhamml, Julia Würfl, Tobias Ploner, Stefan B. Husvogt, Lennart Hohberger, Bettina Fujimoto, James G. Maier, Andreas Sci Rep Article Denoising in optical coherence tomography (OCT) is important to compensate the low signal-to-noise ratio originating from laser speckle. In recent years learning algorithms have been established as the most powerful denoising approach. Especially unsupervised denoising is an interesting topic since it is not possible to acquire noise free scans with OCT. However, speckle in in-vivo OCT images contains not only noise but also information about blood flow. Existing OCT denoising algorithms treat all speckle equally and do not distinguish between the noise component and the flow information component of speckle. Consequently they either tend to either remove all speckle or denoise insufficiently. Unsupervised denoising methods tend to remove all speckle but create results that have a blurry impression which is not desired in a clinical application. To this end we propose the concept, that an OCT denoising method should, besides reducing uninformative noise, additionally preserve the flow-related speckle information. In this work, we present a fully unsupervised algorithm for single-frame OCT denoising (SSN2V) that fulfills these goals by incorporating known operators into our network. This additional constraint greatly improves the denoising capability compared to a network without. Quantitative and qualitative results show that the proposed method can effectively reduce the speckle noise in OCT B-scans of the human retina while maintaining a sharp impression outperforming the compared methods. Nature Publishing Group UK 2023-06-27 /pmc/articles/PMC10299996/ /pubmed/37369731 http://dx.doi.org/10.1038/s41598-023-37324-5 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 | Article Schottenhamml, Julia Würfl, Tobias Ploner, Stefan B. Husvogt, Lennart Hohberger, Bettina Fujimoto, James G. Maier, Andreas SSN2V: unsupervised OCT denoising using speckle split |
title | SSN2V: unsupervised OCT denoising using speckle split |
title_full | SSN2V: unsupervised OCT denoising using speckle split |
title_fullStr | SSN2V: unsupervised OCT denoising using speckle split |
title_full_unstemmed | SSN2V: unsupervised OCT denoising using speckle split |
title_short | SSN2V: unsupervised OCT denoising using speckle split |
title_sort | ssn2v: unsupervised oct denoising using speckle split |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10299996/ https://www.ncbi.nlm.nih.gov/pubmed/37369731 http://dx.doi.org/10.1038/s41598-023-37324-5 |
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