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ADC-Net: An Open-Source Deep Learning Network for Automated Dispersion Compensation in Optical Coherence Tomography
Chromatic dispersion is a common problem to degrade the system resolution in optical coherence tomography (OCT). This study is to develop a deep learning network for automated dispersion compensation (ADC-Net) in OCT. The ADC-Net is based on a modified UNet architecture which employs an encoder-deco...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9024062/ https://www.ncbi.nlm.nih.gov/pubmed/35463032 http://dx.doi.org/10.3389/fmed.2022.864879 |
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author | Ahmed, Shaiban Le, David Son, Taeyoon Adejumo, Tobiloba Ma, Guangying Yao, Xincheng |
author_facet | Ahmed, Shaiban Le, David Son, Taeyoon Adejumo, Tobiloba Ma, Guangying Yao, Xincheng |
author_sort | Ahmed, Shaiban |
collection | PubMed |
description | Chromatic dispersion is a common problem to degrade the system resolution in optical coherence tomography (OCT). This study is to develop a deep learning network for automated dispersion compensation (ADC-Net) in OCT. The ADC-Net is based on a modified UNet architecture which employs an encoder-decoder pipeline. The input section encompasses partially compensated OCT B-scans with individual retinal layers optimized. Corresponding output is a fully compensated OCT B-scan with all retinal layers optimized. Two numeric parameters, i.e., peak signal to noise ratio (PSNR) and structural similarity index metric computed at multiple scales (MS-SSIM), were used for objective assessment of the ADC-Net performance and optimal values of 29.95 ± 2.52 dB and 0.97 ± 0.014 were obtained respectively. Comparative analysis of training models, including single, three, five, seven and nine input channels were implemented. The mode with five-input channels was observed to be optimal for ADC-Net training to achieve robust dispersion compensation in OCT. |
format | Online Article Text |
id | pubmed-9024062 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90240622022-04-23 ADC-Net: An Open-Source Deep Learning Network for Automated Dispersion Compensation in Optical Coherence Tomography Ahmed, Shaiban Le, David Son, Taeyoon Adejumo, Tobiloba Ma, Guangying Yao, Xincheng Front Med (Lausanne) Medicine Chromatic dispersion is a common problem to degrade the system resolution in optical coherence tomography (OCT). This study is to develop a deep learning network for automated dispersion compensation (ADC-Net) in OCT. The ADC-Net is based on a modified UNet architecture which employs an encoder-decoder pipeline. The input section encompasses partially compensated OCT B-scans with individual retinal layers optimized. Corresponding output is a fully compensated OCT B-scan with all retinal layers optimized. Two numeric parameters, i.e., peak signal to noise ratio (PSNR) and structural similarity index metric computed at multiple scales (MS-SSIM), were used for objective assessment of the ADC-Net performance and optimal values of 29.95 ± 2.52 dB and 0.97 ± 0.014 were obtained respectively. Comparative analysis of training models, including single, three, five, seven and nine input channels were implemented. The mode with five-input channels was observed to be optimal for ADC-Net training to achieve robust dispersion compensation in OCT. Frontiers Media S.A. 2022-04-08 /pmc/articles/PMC9024062/ /pubmed/35463032 http://dx.doi.org/10.3389/fmed.2022.864879 Text en Copyright © 2022 Ahmed, Le, Son, Adejumo, Ma and Yao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Medicine Ahmed, Shaiban Le, David Son, Taeyoon Adejumo, Tobiloba Ma, Guangying Yao, Xincheng ADC-Net: An Open-Source Deep Learning Network for Automated Dispersion Compensation in Optical Coherence Tomography |
title | ADC-Net: An Open-Source Deep Learning Network for Automated Dispersion Compensation in Optical Coherence Tomography |
title_full | ADC-Net: An Open-Source Deep Learning Network for Automated Dispersion Compensation in Optical Coherence Tomography |
title_fullStr | ADC-Net: An Open-Source Deep Learning Network for Automated Dispersion Compensation in Optical Coherence Tomography |
title_full_unstemmed | ADC-Net: An Open-Source Deep Learning Network for Automated Dispersion Compensation in Optical Coherence Tomography |
title_short | ADC-Net: An Open-Source Deep Learning Network for Automated Dispersion Compensation in Optical Coherence Tomography |
title_sort | adc-net: an open-source deep learning network for automated dispersion compensation in optical coherence tomography |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9024062/ https://www.ncbi.nlm.nih.gov/pubmed/35463032 http://dx.doi.org/10.3389/fmed.2022.864879 |
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