Automated detection and classification of nuclei in PAX5 and H&E-stained tissue sections of follicular lymphoma. Issue 1 (January 2017)

• Record Type: Journal Article
• Authors: Dimitropoulos, Kosmas; Barmpoutis, Panagiotis; Koletsa, Triantafyllia; Kostopoulos, Ioannis; Grammalidis, Nikos
• Is Part Of: Signal, image and video processing. Volume 11:Issue 1(2017)
• Page Start: 145
• Page End: 153
• Publication Date: 2017-01
• Subjects: Biomedical image processing -- Follicular lymphoma -- Cell segmentation -- Bayesian networks; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-016-0913-6
• Languages: English
• ISSNs: 1863-1703

Abstract

Abstract 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 $$\upmu $$ μ 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 $$94.56\, \%$$ 94.56 % .