Full-view in vivo skin and blood vessels profile segmentation in photoacoustic imaging based on deep learning

Photoacoustic (PA) microscopy allows imaging of the soft biological tissue based on optical absorption contrast and spatial ultrasound resolution. One of the major applications of PA imaging is its characterization of microvasculature. However, the strong PA signal from skin layer overshadowed the s...

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Autores principales:	Cao Duong Ly, Van Tu Nguyen, Tan Hung Vo, Sudip Mondal, Sumin Park, Jaeyeop Choi, Thi Thu Ha Vu, Chang-Seok Kim, Junghwan Oh
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Photoacoustic imaging Segmentation High resolution Deep learning U-Net Physics QC1-999 Acoustics. Sound QC221-246 Optics. Light QC350-467
Acceso en línea:	https://doaj.org/article/e0e7fef8bb85424fa5df303e2b336e73
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id	oai:doaj.org-article:e0e7fef8bb85424fa5df303e2b336e73
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spelling	oai:doaj.org-article:e0e7fef8bb85424fa5df303e2b336e732021-11-12T04:33:33ZFull-view in vivo skin and blood vessels profile segmentation in photoacoustic imaging based on deep learning2213-597910.1016/j.pacs.2021.100310https://doaj.org/article/e0e7fef8bb85424fa5df303e2b336e732022-03-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2213597921000707https://doaj.org/toc/2213-5979Photoacoustic (PA) microscopy allows imaging of the soft biological tissue based on optical absorption contrast and spatial ultrasound resolution. One of the major applications of PA imaging is its characterization of microvasculature. However, the strong PA signal from skin layer overshadowed the subcutaneous blood vessels leading to indirectly reconstruct the PA images in human study. Addressing the present situation, we examined a deep learning (DL) automatic algorithm to achieve high-resolution and high-contrast segmentation for widening PA imaging applications. In this research, we propose a DL model based on modified U-Net for extracting the relationship features between amplitudes of the generated PA signal from skin and underlying vessels. This study illustrates the broader potential of hybrid complex network as an automatic segmentation tool for the in vivo PA imaging. With DL-infused solution, our result outperforms the previous studies with achieved real-time semantic segmentation on large-size high-resolution PA images.Cao Duong LyVan Tu NguyenTan Hung VoSudip MondalSumin ParkJaeyeop ChoiThi Thu Ha VuChang-Seok KimJunghwan OhElsevierarticlePhotoacoustic imagingSegmentationHigh resolutionDeep learningU-NetPhysicsQC1-999Acoustics. SoundQC221-246Optics. LightQC350-467ENPhotoacoustics, Vol 25, Iss , Pp 100310- (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Photoacoustic imaging Segmentation High resolution Deep learning U-Net Physics QC1-999 Acoustics. Sound QC221-246 Optics. Light QC350-467
spellingShingle	Photoacoustic imaging Segmentation High resolution Deep learning U-Net Physics QC1-999 Acoustics. Sound QC221-246 Optics. Light QC350-467 Cao Duong Ly Van Tu Nguyen Tan Hung Vo Sudip Mondal Sumin Park Jaeyeop Choi Thi Thu Ha Vu Chang-Seok Kim Junghwan Oh Full-view in vivo skin and blood vessels profile segmentation in photoacoustic imaging based on deep learning
description	Photoacoustic (PA) microscopy allows imaging of the soft biological tissue based on optical absorption contrast and spatial ultrasound resolution. One of the major applications of PA imaging is its characterization of microvasculature. However, the strong PA signal from skin layer overshadowed the subcutaneous blood vessels leading to indirectly reconstruct the PA images in human study. Addressing the present situation, we examined a deep learning (DL) automatic algorithm to achieve high-resolution and high-contrast segmentation for widening PA imaging applications. In this research, we propose a DL model based on modified U-Net for extracting the relationship features between amplitudes of the generated PA signal from skin and underlying vessels. This study illustrates the broader potential of hybrid complex network as an automatic segmentation tool for the in vivo PA imaging. With DL-infused solution, our result outperforms the previous studies with achieved real-time semantic segmentation on large-size high-resolution PA images.
format	article
author	Cao Duong Ly Van Tu Nguyen Tan Hung Vo Sudip Mondal Sumin Park Jaeyeop Choi Thi Thu Ha Vu Chang-Seok Kim Junghwan Oh
author_facet	Cao Duong Ly Van Tu Nguyen Tan Hung Vo Sudip Mondal Sumin Park Jaeyeop Choi Thi Thu Ha Vu Chang-Seok Kim Junghwan Oh
author_sort	Cao Duong Ly
title	Full-view in vivo skin and blood vessels profile segmentation in photoacoustic imaging based on deep learning
title_short	Full-view in vivo skin and blood vessels profile segmentation in photoacoustic imaging based on deep learning
title_full	Full-view in vivo skin and blood vessels profile segmentation in photoacoustic imaging based on deep learning
title_fullStr	Full-view in vivo skin and blood vessels profile segmentation in photoacoustic imaging based on deep learning
title_full_unstemmed	Full-view in vivo skin and blood vessels profile segmentation in photoacoustic imaging based on deep learning
title_sort	full-view in vivo skin and blood vessels profile segmentation in photoacoustic imaging based on deep learning
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/e0e7fef8bb85424fa5df303e2b336e73
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Full-view in vivo skin and blood vessels profile segmentation in photoacoustic imaging based on deep learning

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