Feature Detection To Segment Cardiomyocyte Nuclei For Investigating Cardiac Contractility

In vivo quantitative assessment of structural and functional biomarkers is essential for understanding pathophysiology and identifying novel therapies for congenital heart disorders. Cardiac defect analysis through fixed tissue and histology has offered revolutionary insights into the tissue architecture, but section thickness limits the tissue penetration. This study demonstrated the potential of Light Sheet Fluorescence Microscopy (LSFM) for analyzing in vivo 4D (3d + time) cardiac contractility. Furthermore, we have described the utility of an improved feature detection framework for localizing cardiomyocyte nuclei in the zebrafish atrium and ventricle. Using the Hessian Difference of Gaussian (HDoG) scale space in conjunction with the watershed algorithm, we were able to quantify a statistically significant increase in cardiomyocyte nuclei count across different developmental stages. Furthermore, we assessed individual volumes and surface areas for the cardiomyocyte nuclei in the ventricles innermost and outermost curvature during cardiac systole and diastole. Using the segmented nuclei volumes from the feature detection, we successfully performed local area ratio analysis to quantify the degree of deformation suffered by the outermost ventricular region compared to the innermost ventricular region. This paper focuses on the merits of our segmentation and demonstrates its efficacy for cell counting and morphology analysis in the presence of anisotropic illumination across the field-of-view (FOV).