Attribute Classification of Cell Nuclei in 3D Myocardial Tissue Images of Mice

In this study, we developed an automated classification method for cell nuclei to facilitate the analysis of three-dimensional myocardial tissue microscopy images, a process that typically demands substantial time and manual effort. The proposed method takes as input z-stack images acquired using confocal microscopy and classifies cell nuclei into three categories—cardiomyocyte nuclei, vascular endothelial cell nuclei, and other cell types—by computing features related to nuclear morphology and physiological characteristics. Two datasets were prepared for evaluation, and the validity of the classification results was assessed not only in terms of accuracy but also with respect to cell type composition and mitotic activity. The experimental results showed that the proposed method achieved high classification scores for both cardiomyocyte and vascular endothelial cell nuclei. Furthermore, the estimated cell type proportions and levels of mitotic activity were found to be largely consistent with previously reported findings in the literature. These results suggest that the proposed method is effective for cell nucleus classification.