Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification
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Stanciu-2014-Experimenting liver fibrosis.pdf
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4.23 MB
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Author(s)
Stanciu, Stefan G.
Xu, Shuoyu
Peng, Qiwen
Yan, Jie
Stanciu, George A.
Welsch, Roy E.
So, Peter T. C.
Csucs, Gabor
Yu, Hanry
Date Issued
April 2014
Journal
Scientific Reports
Publisher
Nature Publishing Group
Citation
Stanciu, Stefan G., Shuoyu Xu, Qiwen Peng, Jie Yan, George A. Stanciu, Roy E. Welsch, Peter T. C. So, Gabor Csucs, and Hanry Yu. “Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification.” Sci. Rep. 4 (April 10, 2014).
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Mechanical Engineering
Sloan School of Management
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Creative Commons Attribution-NonCommercial-NoDerivs 3.0
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DOI of Published Version
https://doi.org/10.1038/srep04636
