Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification

The accurate staging of liver fibrosis is of paramount importance to determine the state of disease progression, therapy responses, and to optimize disease treatment strategies. Non-linear optical microscopy techniques such as two-photon excitation fluorescence (TPEF) and second harmonic generation...

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Main Authors:	Stanciu, Stefan G., Xu, Shuoyu, Peng, Qiwen, Yan, Jie, Stanciu, George A., Welsch, Roy E., So, Peter T. C., Csucs, Gabor, Yu, Hanry
Format:	Online
Language:	English
Published:	Nature Publishing Group 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3982167/

id	pubmed-3982167
recordtype	oai_dc
spelling	pubmed-39821672014-04-10 Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification Stanciu, Stefan G. Xu, Shuoyu Peng, Qiwen Yan, Jie Stanciu, George A. Welsch, Roy E. So, Peter T. C. Csucs, Gabor Yu, Hanry Article The accurate staging of liver fibrosis is of paramount importance to determine the state of disease progression, therapy responses, and to optimize disease treatment strategies. Non-linear optical microscopy techniques such as two-photon excitation fluorescence (TPEF) and second harmonic generation (SHG) can image the endogenous signals of tissue structures and can be used for fibrosis assessment on non-stained tissue samples. While image analysis of collagen in SHG images was consistently addressed until now, cellular and tissue information included in TPEF images, such as inflammatory and hepatic cell damage, equally important as collagen deposition imaged by SHG, remain poorly exploited to date. We address this situation by experimenting liver fibrosis quantification and scoring using a combined approach based on TPEF liver surface imaging on a Thioacetamide-induced rat model and a gradient based Bag-of-Features (BoF) image classification strategy. We report the assessed performance results and discuss the influence of specific BoF parameters to the performance of the fibrosis scoring framework. Nature Publishing Group 2014-04-10 /pmc/articles/PMC3982167/ /pubmed/24717650 http://dx.doi.org/10.1038/srep04636 Text en Copyright © 2014, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by-nc-nd/3.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported license. The images in this article are included in the article's Creative Commons license, unless indicated otherwise in the image credit; if the image is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the image. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Stanciu, Stefan G. Xu, Shuoyu Peng, Qiwen Yan, Jie Stanciu, George A. Welsch, Roy E. So, Peter T. C. Csucs, Gabor Yu, Hanry
spellingShingle	Stanciu, Stefan G. Xu, Shuoyu Peng, Qiwen Yan, Jie Stanciu, George A. Welsch, Roy E. So, Peter T. C. Csucs, Gabor Yu, Hanry Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification
author_facet	Stanciu, Stefan G. Xu, Shuoyu Peng, Qiwen Yan, Jie Stanciu, George A. Welsch, Roy E. So, Peter T. C. Csucs, Gabor Yu, Hanry
author_sort	Stanciu, Stefan G.
title	Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification
title_short	Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification
title_full	Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification
title_fullStr	Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification
title_full_unstemmed	Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification
title_sort	experimenting liver fibrosis diagnostic by two photon excitation microscopy and bag-of-features image classification
description	The accurate staging of liver fibrosis is of paramount importance to determine the state of disease progression, therapy responses, and to optimize disease treatment strategies. Non-linear optical microscopy techniques such as two-photon excitation fluorescence (TPEF) and second harmonic generation (SHG) can image the endogenous signals of tissue structures and can be used for fibrosis assessment on non-stained tissue samples. While image analysis of collagen in SHG images was consistently addressed until now, cellular and tissue information included in TPEF images, such as inflammatory and hepatic cell damage, equally important as collagen deposition imaged by SHG, remain poorly exploited to date. We address this situation by experimenting liver fibrosis quantification and scoring using a combined approach based on TPEF liver surface imaging on a Thioacetamide-induced rat model and a gradient based Bag-of-Features (BoF) image classification strategy. We report the assessed performance results and discuss the influence of specific BoF parameters to the performance of the fibrosis scoring framework.
publisher	Nature Publishing Group
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3982167/
_version_	1612076575063277568

Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification

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