SSN2V: unsupervised OCT denoising using speckle split
Abstract 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 top...
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| Format: | Article |
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Nature Portfolio
2023-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-37324-5 |
| _version_ | 1797789796724113408 |
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| author | Julia Schottenhamml Tobias Würfl Stefan B. Ploner Lennart Husvogt Bettina Hohberger James G. Fujimoto Andreas Maier |
| author_facet | Julia Schottenhamml Tobias Würfl Stefan B. Ploner Lennart Husvogt Bettina Hohberger James G. Fujimoto Andreas Maier |
| author_sort | Julia Schottenhamml |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-13T01:55:45Z |
| format | Article |
| id | doaj.art-b249f726579247ca8b61a08cdb781f2c |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-13T01:55:45Z |
| publishDate | 2023-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b249f726579247ca8b61a08cdb781f2c2023-07-02T11:15:54ZengNature PortfolioScientific Reports2045-23222023-06-0113111210.1038/s41598-023-37324-5SSN2V: unsupervised OCT denoising using speckle splitJulia Schottenhamml0Tobias Würfl1Stefan B. Ploner2Lennart Husvogt3Bettina Hohberger4James G. Fujimoto5Andreas Maier6Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-NürnbergPattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-NürnbergPattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-NürnbergPattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-NürnbergDepartment of Ophthalmology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-NürnbergResearch Laboratory of Electronics, Department of Electrical Engineering and Computer Science, Massachusetts Institute of TechnologyPattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-NürnbergAbstract 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.https://doi.org/10.1038/s41598-023-37324-5 |
| spellingShingle | Julia Schottenhamml Tobias Würfl Stefan B. Ploner Lennart Husvogt Bettina Hohberger James G. Fujimoto Andreas Maier SSN2V: unsupervised OCT denoising using speckle split Scientific Reports |
| 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 |
| url | https://doi.org/10.1038/s41598-023-37324-5 |
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