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

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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 \(\upmu \)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 \(94.56\,\%\).

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Authors and Affiliations

  1. Information Technologies Institute, Center for Research and Technology Hellas, 57001, Thermi, Thessaloniki, Greece

    Kosmas Dimitropoulos, Panagiotis Barmpoutis & Nikos Grammalidis

  2. Pathology Department, Medical School, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece

    Triantafyllia Koletsa & Ioannis Kostopoulos

Authors
  1. Kosmas Dimitropoulos
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  2. Panagiotis Barmpoutis
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  3. Triantafyllia Koletsa
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  4. Ioannis Kostopoulos
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  5. Nikos Grammalidis
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Correspondence to Nikos Grammalidis.

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Dimitropoulos, K., Barmpoutis, P., Koletsa, T. et al. Automated detection and classification of nuclei in PAX5 and H&E-stained tissue sections of follicular lymphoma. SIViP 11, 145–153 (2017). https://doi.org/10.1007/s11760-016-0913-6

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  • DOI: https://doi.org/10.1007/s11760-016-0913-6

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