Machine learning algorithms and software to quantify immunohistochemical images in biomedicine

Abstract

Oral cancer can develop from potentially malignant disorders such as oral leukoplakia. Immunohistochemical staining is a complementary technique to improve the diagnosis and prognosis of oral leukoplakia. Cell counting in these images was traditionally performed manually, which was very laborious. Accurate computer-aided cell detection in immunohistochemistry images of different tissues is essential for advancing digital pathology and enabling large-scale quantitative analysis. This thesis focus on the analysis of immunohistochemical (IHC) images of mouth tissue, developping software and proposing image analysis algorithms, in order to provide new skills to the diagnosis and prognosis of oral cancer. OralImmunoAnalyser is presented, which is a new reliable and easy-to-use software tool to estimate the oral leukoplakia from the IHC images of mouth tissues, which is available from the CiTIUS repository for research purposes.