Prediction of cancer cell nuclear centers in immunohistochemical fluorescence images
https://doi.org/10.37661/1816-0301-2026-23-2-21-38
Abstract
Objectives. The aim of the study is to develop a method for predicting cancer cell nuclear centers in immunohistochemical fluorescence images using point annotation of nuclear centers.
Methods. Deep learning convolutional neural networks are used in this study.
Results. A method for predicting cancer cell nuclear centers in immunohistochemical fluorescence images of diseased tissues is proposed. The method differs from existing approaches by using point annotation of nuclear centers during the learning process. An algorithm for image pre- and postprocessing has been developed, enabling an end-to-end analysis for images of any dimension.
Conclusion. A method for predicting cancer cell nuclear centers in immunohistochemical fluorescence images has been developed. It has a simple architecture, a small number of trainable parameters, and does not require complex post-processing of analysis results, traditionally involved in semantic segmentation for separating clustered nuclei. The method allows for counting the number of cancer cells per unit area, which in turn makes it possible to assess the extent of the disease. The total analysis time for a 2048×2048 pixel image using the T4 (Google Colab) compute engine averages 750 ms, enabling the analysis of high-dimensional, whole-slide images in a reasonable time.
About the Authors
V. V. Skakun
Belarusian State University
Belarus
Belarus
Victor V. Skakun, Cand. Sci. (Phys.-Math.), Assoc. Prof., Head of Department System Analysis and Computer Modeling
av. Nezavisimosti, 4, Minsk, 220030
S. Xu
Belarusian State University
Belarus
Belarus
Silun Xu, Applicant at the Department of System Analysis and Computer Modeling
av. Nezavisimosti, 4, Minsk, 220030
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Review
For citations:
Skakun V.V., Xu S. Prediction of cancer cell nuclear centers in immunohistochemical fluorescence images. Informatics. 2026;23(2):21-38. (In Russ.) https://doi.org/10.37661/1816-0301-2026-23-2-21-38
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