Abstract
At sites of chronic inflammation caused by persistent microbial infection or foreign objects, macrophages can fuse forming multinucleated giant cells. On implanted materials, these foreign body-type giant cells (FBGC) are associated with material degradation and device failure. Thus, formation of multinucleated cells is of relevance for the biomaterials field, particularly for the design of novel strategies for bone regeneration. After cell differentiation on varying conditions, multinucleation is analyzed by an independent observer and the percentage of fusion quantified. However, this analysis is still mostly performed manually, being time consuming and prone to errors.
We propose a fully automated method for nuclei detection and the nuclei groups formed in each giant cell. To perform such analysis with robustness and increased performance we use the Laplacian of Gaussian local image filter for cell nuclei detection and Delaunay graphs for the analysis of the formation of nuclei groups characteristic of giant multinucleated cells. Nuclei groups are detected based on both distance between cell nuclei and the properties of the cytoplasm between them.
Results show a good performance in nuclei detection in these images (87.6%), with reasonable error level in giant cell detection (22%), indicating some difficulty in analyzing this data.
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Ferro, L. et al. (2013). Multinuclear Cell Analysis Using Laplacian of Gaussian and Delaunay Graphs. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_52
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DOI: https://doi.org/10.1007/978-3-642-38628-2_52
Publisher Name: Springer, Berlin, Heidelberg
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