Automated quantitative analysis of multiple cardiomyocytes at the single‐cell level with three‐dimensional holographic imaging informatics

Cardiomyocytes derived from human pluripotent stem cells are a promising tool for disease modeling, drug compound testing, and cardiac toxicity screening. Bio‐image segmentation is a prerequisite step in cardiomyocyte image analysis by digital holography (DH) in microscopic configuration and has provided satisfactory results. In this study, we quantified multiple cardiac cells from segmented 3‐dimensional DH images at the single‐cell level and measured multiple parameters describing the beating profile of each individual cell. The beating profile is extracted by monitoring dry‐mass distribution during the mechanical contraction‐relaxation activity caused by cardiac action potential. We present a robust two‐step segmentation method for cardiomyocyte low‐contrast image segmentation based on region and edge information. The segmented single‐cell contains mostly the nucleus of the cell since it is the best part of the cardiac cell, which can be perfectly segmented. Clustering accuracy was assessed by a silhouette index evaluation for k‐means clustering and the Dice similarity coefficient (DSC) of the final segmented image. 3D representation of single of cardiomyocytes. The cell contains mostly the nucleus section and a small area of cytoplasm.