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 (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.
1] Center for Microscopy-Microanalysis and Information Processing, University Politehnica of Bucharest, Romania [2] Light Microscopy and Screening Center, ETH Zurich, Switzerland.
Stefan G
SG
Stanciu
1] Computation and System Biology Program, Singapore MIT Alliance, Singapore, Singapore [2] Biosystems and Micromechanics IRG, Singapore MIT Alliance for Research and Technology, Singapore, Singapore [3] Institute of Bioengineering and Nanotechnology, Singapore, Singapore.
Shuoyu
S
Xu
1] Computation and System Biology Program, Singapore MIT Alliance, Singapore, Singapore [2] Institute of Bioengineering and Nanotechnology, Singapore, Singapore.
Qiwen
Q
Peng
1] Biosystems and Micromechanics IRG, Singapore MIT Alliance for Research and Technology, Singapore, Singapore [2] Institute of Bioengineering and Nanotechnology, Singapore, Singapore.
Jie
J
Yan
Center for Microscopy-Microanalysis and Information Processing, University Politehnica of Bucharest, Romania.
George A
GA
Stanciu
1] Computation and System Biology Program, Singapore MIT Alliance, Singapore, Singapore [2] Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA.
Roy E
RE
Welsch
1] Computation and System Biology Program, Singapore MIT Alliance, Singapore, Singapore [2] Biosystems and Micromechanics IRG, Singapore MIT Alliance for Research and Technology, Singapore, Singapore [3] Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA [4] Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Peter T C
PT
So
Light Microscopy and Screening Center, ETH Zurich, Switzerland.
Gabor
G
Csucs
1] Computation and System Biology Program, Singapore MIT Alliance, Singapore, Singapore [2] Biosystems and Micromechanics IRG, Singapore MIT Alliance for Research and Technology, Singapore, Singapore [3] Institute of Bioengineering and Nanotechnology, Singapore, Singapore [4] Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA [5] Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore [6] The Mechanobiology Institute, Singapore, Singapore.
Hanry
H
Yu
D000818:Animals; D018450:Disease Progression; D007091:Image Processing, Computer-Assisted; D008099:Liver / Q000473:pathology; D008103:Liver Cirrhosis / Q000145:classification / Q000175:diagnosis*; D008297:Male; D036641:Microscopy, Fluorescence, Multiphoton / Q000379:methods*; D051381:Rats; D017208:Rats, Wistar; D013853:Thioacetamide
D013853:Thioacetamide