Synthetic polarization-sensitive optical coherence tomography by deep learning

Abstract Polarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution label-free optical biomedical imaging modality that is sensitive to the microstructural architecture in tissue that gives rise to form birefringence, such as collagen or muscle fibers. To enable polarization s...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Yi Sun, Jianfeng Wang, Jindou Shi, Stephen A. Boppart
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/a5bc8ef4638c4706a63612db7ea60cc4
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a5bc8ef4638c4706a63612db7ea60cc4
record_format	dspace
spelling	oai:doaj.org-article:a5bc8ef4638c4706a63612db7ea60cc42021-12-02T16:10:30ZSynthetic polarization-sensitive optical coherence tomography by deep learning10.1038/s41746-021-00475-82398-6352https://doaj.org/article/a5bc8ef4638c4706a63612db7ea60cc42021-07-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00475-8https://doaj.org/toc/2398-6352Abstract Polarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution label-free optical biomedical imaging modality that is sensitive to the microstructural architecture in tissue that gives rise to form birefringence, such as collagen or muscle fibers. To enable polarization sensitivity in an OCT system, however, requires additional hardware and complexity. We developed a deep-learning method to synthesize PS-OCT images by training a generative adversarial network (GAN) on OCT intensity and PS-OCT images. The synthesis accuracy was first evaluated by the structural similarity index (SSIM) between the synthetic and real PS-OCT images. Furthermore, the effectiveness of the computational PS-OCT images was validated by separately training two image classifiers using the real and synthetic PS-OCT images for cancer/normal classification. The similar classification results of the two trained classifiers demonstrate that the predicted PS-OCT images can be potentially used interchangeably in cancer diagnosis applications. In addition, we applied the trained GAN models on OCT images collected from a separate OCT imaging system, and the synthetic PS-OCT images correlate well with the real PS-OCT image collected from the same sample sites using the PS-OCT imaging system. This computational PS-OCT imaging method has the potential to reduce the cost, complexity, and need for hardware-based PS-OCT imaging systems.Yi SunJianfeng WangJindou ShiStephen A. BoppartNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Yi Sun Jianfeng Wang Jindou Shi Stephen A. Boppart Synthetic polarization-sensitive optical coherence tomography by deep learning
description	Abstract Polarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution label-free optical biomedical imaging modality that is sensitive to the microstructural architecture in tissue that gives rise to form birefringence, such as collagen or muscle fibers. To enable polarization sensitivity in an OCT system, however, requires additional hardware and complexity. We developed a deep-learning method to synthesize PS-OCT images by training a generative adversarial network (GAN) on OCT intensity and PS-OCT images. The synthesis accuracy was first evaluated by the structural similarity index (SSIM) between the synthetic and real PS-OCT images. Furthermore, the effectiveness of the computational PS-OCT images was validated by separately training two image classifiers using the real and synthetic PS-OCT images for cancer/normal classification. The similar classification results of the two trained classifiers demonstrate that the predicted PS-OCT images can be potentially used interchangeably in cancer diagnosis applications. In addition, we applied the trained GAN models on OCT images collected from a separate OCT imaging system, and the synthetic PS-OCT images correlate well with the real PS-OCT image collected from the same sample sites using the PS-OCT imaging system. This computational PS-OCT imaging method has the potential to reduce the cost, complexity, and need for hardware-based PS-OCT imaging systems.
format	article
author	Yi Sun Jianfeng Wang Jindou Shi Stephen A. Boppart
author_facet	Yi Sun Jianfeng Wang Jindou Shi Stephen A. Boppart
author_sort	Yi Sun
title	Synthetic polarization-sensitive optical coherence tomography by deep learning
title_short	Synthetic polarization-sensitive optical coherence tomography by deep learning
title_full	Synthetic polarization-sensitive optical coherence tomography by deep learning
title_fullStr	Synthetic polarization-sensitive optical coherence tomography by deep learning
title_full_unstemmed	Synthetic polarization-sensitive optical coherence tomography by deep learning
title_sort	synthetic polarization-sensitive optical coherence tomography by deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a5bc8ef4638c4706a63612db7ea60cc4
work_keys_str_mv	AT yisun syntheticpolarizationsensitiveopticalcoherencetomographybydeeplearning AT jianfengwang syntheticpolarizationsensitiveopticalcoherencetomographybydeeplearning AT jindoushi syntheticpolarizationsensitiveopticalcoherencetomographybydeeplearning AT stephenaboppart syntheticpolarizationsensitiveopticalcoherencetomographybydeeplearning
_version_	1718384429776240640

Synthetic polarization-sensitive optical coherence tomography by deep learning

Ejemplares similares