Automated detection and classification of nuclei in PAX5 and H&E-stained tissue sections of follicular lymphoma

In this paper, we propose a novel framework for the detection and classification of centroblasts (CB) in follicular lymphoma (FL) tissue samples stained with PAX5 and H&E stains and sliced at 1 m thickness level. By employing PAX5 immunohistochemistry, we facilitate the segmentation of nuclei, while the use of H&E stain enables us to extract textural information related to histological characteristics used by pathologists in the diagnosis of FL grading. For the segmentation of nuclei in PAX5-stained images, we initially apply an energy minimization technique based on graph cuts and then we propose a novel algorithm for the separation of overlapped nuclei inspired by the clustering of large-scale visual vocabularies. The morphological characteristics of nuclei extracted from PAX5-stained images are combined with a number of textural characteristics identified in H&E images through a Bayesian network classifier, which aims to model pathologists' knowledge used in FL grading. Experimental results have already shown the great potential of the proposed methodology providing an average F-score of 94.56%.