Abstract
Automated centroblast (CB) detection in Follicular Lymphoma (FL) tissue samples has recently attracted significant research interest. Most of the methods described in the literature are based on the use of Hematoxilin and Eosin (H&E) stain. However, the automated detection of CBs from H&E stained images remains a challenging issue. To this end, this paper presents a novel approach which is based on the use of both PAX5 and H&E stains in tissue sections sliced at the thickness of 1μm. The goal of PAX5 is three-fold: to facilitate the segmentation of nuclei, to remove a number of follicular dendritic cells and finally to extract morphological characteristics of nuclei. Furthermore, the use of H&E stain enables us to extract textural information related to histological characteristics used by pathologists in diagnosis of FL grading. In our method we propose a novel algorithm for the separation of overlapped nuclei inspired by the clustering of large scale visual vocabularies. Finally, aiming to model pathologists’ knowledge used in FL grading, we use a Bayesian Network classifier to combine the morphological and textural characteristics. Experiments conducted on a dataset of ten pairs of PAX5 and H&E images demonstrate the potential of the proposed approach providing an average detection rate of 93.46%.
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Dimitropoulos, K., Barmpoutis, P., Koletsa, T., Kostopoulos, I., Grammalidis, N. (2016). Classification of Nuclei in Follicular Lyphoma Tissue Sections Using Different Stains and Bayesian Networks. In: Kyriacou, E., Christofides, S., Pattichis, C. (eds) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IFMBE Proceedings, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-32703-7_47
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DOI: https://doi.org/10.1007/978-3-319-32703-7_47
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