Premium
Automated quantitative analysis of multiple cardiomyocytes at the single‐cell level with three‐dimensional holographic imaging informatics
Author(s) -
Moon Inkyu,
Jaferzadeh Keyvan,
Ahmadzadeh Ezat,
Javidi Bahram
Publication year - 2018
Publication title -
journal of biophotonics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.877
H-Index - 66
eISSN - 1864-0648
pISSN - 1864-063X
DOI - 10.1002/jbio.201800116
Subject(s) - segmentation , artificial intelligence , image segmentation , cluster analysis , computer science , rand index , cell , pattern recognition (psychology) , computer vision , biomedical engineering , chemistry , medicine , biochemistry
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.