Comparison between a deep-learning and a pixel-based approach for the automated quantification of HIV target cells in foreskin tissue.

Autor: Shao Z; Department of Microbiology and Immunology, The University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada., Buchanan LB; Department of Microbiology and Immunology, The University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada., Zuanazzi D; Department of Microbiology and Immunology, The University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada., Khan YN; Department of Microbiology and Immunology, The University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada., Khan AR; Department of Medical Biophysics, The University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada., Prodger JL; Department of Microbiology and Immunology, The University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada. jprodge@uwo.ca.; Department of Epidemiology and Biostatistics, The University of Western Ontario, 1151 Richmond St, London, ON, N6A 3K7, Canada. jprodge@uwo.ca.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Jan 23; Vol. 14 (1), pp. 1985. Date of Electronic Publication: 2024 Jan 23.
DOI: 10.1038/s41598-024-52613-3
Abstrakt: The availability of target cells expressing the HIV receptors CD4 and CCR5 in genital tissue is a critical determinant of HIV susceptibility during sexual transmission. Quantification of immune cells in genital tissue is therefore an important outcome for studies on HIV susceptibility and prevention. Immunofluorescence microscopy allows for precise visualization of immune cells in mucosal tissues; however, this technique is limited in clinical studies by the lack of an accurate, unbiased, high-throughput image analysis method. Current pixel-based thresholding methods for cell counting struggle in tissue regions with high cell density and autofluorescence, both of which are common features in genital tissue. We describe a deep-learning approach using the publicly available StarDist method to count cells in immunofluorescence microscopy images of foreskin stained for nuclei, CD3, CD4, and CCR5. The accuracy of the model was comparable to manual counting (gold standard) and surpassed the capability of a previously described pixel-based cell counting method. We show that the performance of our deep-learning model is robust in tissue regions with high cell density and high autofluorescence. Moreover, we show that this deep-learning analysis method is both easy to implement and to adapt for the identification of other cell types in genital mucosal tissue.
(© 2024. The Author(s).)
Databáze: MEDLINE
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