Automatic scale-independent morphology-based quantification of liver fibrosis

The pathologists have an expert knowledge of the classification of fibrosis. However, the differentiation of intermediate grades (ex: F2-F3) may cause significant inter-expert variability. A quantitative morphological marker is presented in this paper, introducing a local-based image analysis on human liver tissue slides. Having defined hotspots in slides, the liver collagen is segmented with a color deconvolution technique. After removing the regions of interstitial fibrosis, the fractal dimension of the fibrosis regions is computed by using the boxcounting algorithm. As a result, a quantitative index provides information about the grade of the fibrosis regions and thus about the tissue damage. The index does not take account of the pathological status of the patient but it allows to discriminate accurately and objectively the intermediate grades for which the expert evaluation is partially based on the fibrosis development. This method was used on twelve human liver biopsies (from six different patients) using constant conditions of preparation, acquisition (same image resolution, magnification x20) and box-counting parameters. The liver tissue slides were labeled by a pathologist using METAVIR scores. A reasonably good correlation is observed between the METAVIR scores and the proposed morphological index (p-value < 0:001). Furthermore, the method is reproducible and scale independent which is appropriate for biological high resolution images. Nevertheless, further work is needed to define reference values for this index in such a way that METAVIR subdomains will be well delimited.