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Application of computer-assisted image analysis for identifying and quantifying liver fibrosis in an experimental model

doi: 10.6062/jcis.2011.02.02.0041 (Free PDF)


Luciana Barros Sant'Anna , Nilson Sant'Anna and Ornella Parolini


Liver fibrosis and resultant cirrhosis are amongst the most common outcomes of chronic liver diseases. Currently, liver transplantation remains the only effective treatment. Thus, a reliable and objective method is fundamental for quantifying fibrosis adequately, which is essential to prognosis, diagnosis and evaluation of response to antifibrotic therapies. Visual analysis of biological samples using semiquantitative scoring systems have been described, but all are time-consuming, qualitative, and produce partially subjective fibrosis evaluations with only moderate accuracy. While numerous commercial software packages exist for image analysis, many of these packages are designed for very specific purposes, are expensive and proprietary, meaning that the underlying methods of analysis are hidden from the researcher. On the other hand, the design of the free software ImageJ/National Institutes of Health (NIH) Image is geared more toward the analysis of individual images (comparable to Adobe Photoshop) rather than flexible, high-throughput work. Toward the aim of identifying alternative analytical approaches for precise quantification of a large number of histological images of liver fibrosis, this paper describes the configuration and use of an open-source automated image analysis software, CellProfiler, for quantification of fibrosis induced in rats. Once fibrosis had been established, liver samples were collected, histologically processed and subjected to CellProfiler image analysis, which automatically identifies and isolates fibrosis according to staining, and then measures the area occupied by fibrosis over the total liver area examined. CellProfiler was shown to be an objective, precise and rapid method that allowed simultaneous quantification of fibrosis in all six hundred histological images of injured liver examined, at a rate of  10 s/image. This novel tool might be of special value to allow the drawing of valid conclusions regarding the applicability of regenerative therapies to treat liver fibrosis in experimental studies, and also opens the way for further investigations aimed the extending the use of CellProfiler to other tissue assays.


computational biology, automated image analysis, image processing, CellProfiler software, liver fibrosis.


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