Fibro-C-Index: comprehensive, morphology-based quantification of liver fibrosis using second harmonic generation and two-photon microscopy

Fibro-C-Index: comprehensive, morphology-based quantification of liver fibrosis using second harmonic generation and two-photon microscopy

We develop a standardized, fully automated, quantification system for liver fibrosis assessment using second harmonic generation microscopy and a morphology-based quantification algorithm. Liver fibrosis is associated with an abnormal increase in collagen as a result of chronic liver diseases. Histo...

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Main Authors: Yu, Hanry (Contributor), So, Peter T. C. (Contributor), Rajapakse, Jagath (Contributor), Chen, Chien Shing (Author), Tang, Hui-Huan (Author), Chang, Shi (Author), Xiao, Guangfa (Author), Raja, Anju Mythreyi (Author), Wei, Chiang Li (Author), Wee, Aileen (Author), Cheng, Chee Leong (Author), Chia, Ser-Mien (Author), Kang, Chiang Huen (Author), Xu, Shuoyu (Author), Tan, Nancy (Author), Tai, Dean C. S. (Author)
Other Authors: Massachusetts Institute of Technology. Department of Biological Engineering (Contributor), Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor)
Format: Article
Language:English
Published: Society of Photo-Optical Instrumentation Engineers, 2010-03-16T19:54:26Z.
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Summary:We develop a standardized, fully automated, quantification system for liver fibrosis assessment using second harmonic generation microscopy and a morphology-based quantification algorithm. Liver fibrosis is associated with an abnormal increase in collagen as a result of chronic liver diseases. Histopathological scoring is the most commonly used method for liver fibrosis assessment, where a liver biopsy is stained and scored by experienced pathologists. Due to the intrinsic limited sensitivity and operator-dependent variations, there exist high inter- and intraobserver discrepancies. We validate our quantification system, Fibro-C-Index, with a comprehensive animal study and demonstrate its potential application in clinical diagnosis to reduce inter- and intraobserver discrepancies.
Singapore-MIT Alliance Computational and Systems Biology Flagship Project
Janssen Cilag (grant R-185-000-182-592)
National Medical Research Council (grant R-185-000-099-213)
Ministry of Education (grant R-185-000-135-112)
Biomedical Research Council (grant R185-001-045-305)
A*STAR of Singapore
Institute of Bioengineering and Nanotechnology

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