Journal of Pathology Informatics Journal of Pathology Informatics
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Year : 2013  |  Volume : 4  |  Issue : 1  |  Page : 36

Color correction for automatic fibrosis quantification in liver biopsy specimens

1 Global Scientific Information and Computing Center, Tokyo Institute of Technology, Meguro-ku, Tokyo, Japan
2 Department of Pathology, School of Medicine, Keio University, Shinjyuku-ku, Tokyo, Japan
3 Medical Solutions Division, BioMedical Imaging and Informatics Group, NEC Corporation, Minato-ku, Tokyo, Japan

Correspondence Address:
Yuri Murakami
Global Scientific Information and Computing Center, Tokyo Institute of Technology, Meguro-ku, Tokyo
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/2153-3539.124009

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Context: For a precise and objective quantification of liver fibrosis, quantitative evaluations through image analysis have been utilized. However, manual operations are required in most cases for extracting fiber areas because of color variation included in digital pathology images. Aims: The purpose of this research is to propose a color correction method for whole slide images (WSIs) of Elastica van Gieson (EVG) stained liver biopsy tissue specimens and to realize automated operation of image analysis for fibrosis quantification. Materials and Methods: Our experimental dataset consisted of 38 WSIs of liver biopsy specimens collected from 38 chronic viral hepatitis patients from multiple medical facilities, stained with EVG and scanned at ×20 using a Nano Zoomer 2.0 HT (Hamamatsu Photonics K.K., Hamamatsu, Japan). Color correction was performed by modifying the color distribution of a target WSI so as to fit to the reference, where the color distribution was modeled by a set of two triangle pyramids. Using color corrected WSIs; fibrosis quantification was performed based on tissue classification analysis. Statistical Analysis Used: Spearman's rank correlation coefficients were calculated between liver stiffness measured by transient elastography and median area ratio of collagen fibers calculated based on tissue classification results. Results: Statistical analysis results showed a significant correlation r = 0.61-0.68 even when tissue classifiers were trained by using a subset of WSIs, while the correlation coefficients were reduced to r = 0.40-0.50 without color correction. Conclusions: Fibrosis quantification accompanied with the proposed color correction method could provide an objective evaluation tool for liver fibrosis, which complements semi-quantitative histologic evaluation systems.

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