Colour is the most important feature used in quantitative immunohistochemistry (IHC) image analysis; IHC is used to provide information relating to aetiology and to confirm malignancy. Statistical modelling is a technique widely used for colour detection in computer vision. We have developed a statistical model of colour detection applicable to detection of stain colour in digital IHC images. Model was first trained by massive colour pixels collected semi-automatically. To speed up the training and detection processes, we removed luminance channel, Y channel of YCbCr colour space and chose 128 histogram bins which is the optimal number. A maximum likelihood classifier is used to classify pixels in digital slides into positively or negatively stained pixels automatically. The model-based tool was developed within ImageJ to quantify targets identified using IHC and histochemistry. The purpose of evaluation was to compare the computer model with human evaluation. Several large datasets were prepared and obtained from human oesophageal cancer, colon cancer and liver cirrhosis with different colour stains. Experimental results have demonstrated the model-based tool achieves more accurate results than colour deconvolution and CMYK model in the detection of brown colour, and is comparable to colour deconvolution in the detection of pink colour. We have also demostrated the proposed model has little inter-dataset variations. A robust and effective statistical model is introduced in this paper. The model-based interactive tool in ImageJ, which can create a visual representation of the statistical model and detect a specified colour automatically, is easy to use and available freely at http://rsb.info.nih.gov/ij/plugins/ihc-toolbox/index.html . Testing to the tool by different users showed only minor inter-observer variations in results.

Chronic infection with hepatitis B virus (HBV) progresses through multiple phases, including immune tolerant, immune active, immune control, and, in a subset of patients who achieve immune control, reactivation. The first, the immune tolerant phase, is considered to be prolonged in duration but essentially benign in nature, lacking long-term consequences, and thus not recommended for antiviral therapy. This review challenges the notion that the immune tolerant phase is truly benign and considers the possibility that events during this phase may contribute significantly to cirrhosis, hepatocellular carcinoma (HCC), and the premature death of 25% of HBV carriers worldwide. Thus, earlier treatment than recommended by current guidelines should be considered. Low therapeutic coverage exacerbated by restrictive treatment guidelines may facilitate disease progression in many patients but also increase the risk of neonatal and horizontal transmission from untreated mothers to their children. While a prophylactic vaccine exists, there are many areas worldwide where the treatment of adults and the delivery of an effective vaccination course to newborns present difficult challenges.

Fibrosis and organ failure is a common endpoint for many chronic liver diseases. Much is known about the upstream inflammatory mechanisms provoking fibrosis and downstream potential for tissue remodeling. However, less is known about the transcriptional regulation in vivo governing fibrotic matrix deposition by liver myofibroblasts. This gap in understanding has hampered molecular predictions of disease severity and clinical progression and restricted targets for antifibrotic drug development. In this study, we show the prevalence of SOX9 in biopsies from patients with chronic liver disease correlated with fibrosis severity and accurately predicted disease progression toward cirrhosis. Inactivation of Sox9 in mice protected against both parenchymal and biliary fibrosis, and improved liver function and ameliorated chronic inflammation. SOX9 was downstream of mechanosignaling factor, YAP1. These data demonstrate a role for SOX9 in liver fibrosis and open the way for the transcription factor and its dependent pathways as new diagnostic, prognostic, and therapeutic targets in patients with liver fibrosis.

Aims: The aim of this study was to determine if elastin content in needle core native liver biopsies was predictive of clinical outcome in patients with chronic hepatitis C virus‐related chronic liver disease.Methods and results: Elastin contents in liver biopsies were determined by image analysis, technically validated in an independent centre, and correlated with outcome in patients with advanced (Ishak stage ≥5) chronic hepatitis C virus‐related chronic liver disease. Elastin was robustly quantified in an operator‐independent and laboratory‐independent manner, with very strong correlation of elastin staining measured with two methods of image classification (r_s = 0.873, P < 0.00001). Elastin content (but not absolute scar content or Ishak stage) was predictive for future clinical outcomes. In a cohort of patients without sustained virological response, the median hepatic elastin content was 3.4%, and 17 patients (57%) progressed to a liver‐related clinical outcome; 11 of the 15 patients (73%) with a hepatic elastin content of >3.4% progressed to a clinical outcome, as compared with only six of 15 (40%) with an elastin content of <3.4%. The difference in time to outcome was significant.Conclusions: We describe a simple and reproducible method for elastin quantification in liver biopsies that provides potentially valuable prognostic information to inform clinical management.

In this paper we analyze a fibrosis scoring method based on measurement of the fibrillar collagen area from second harmonic generation (SHG) microscopy images of unstained histological slices from human liver biopsies. The study is conducted on a cohort of one hundred chronic hepatitis C patients with intermediate to strong Metavir and Ishak stages of liver fibrosis. We highlight a key parameter of our scoring method to discriminate between high and low fibrosis stages. Moreover, according to the intensity histograms of the SHG images and simple mathematical arguments, we show that our area-based method is equivalent to an intensity-based method, despite saturation of the images. Finally we propose an improvement of our scoring method using very simple image processing tools.