Abstract
Human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs) beating can be efficiently characterized by time-lapse quantitative phase imaging (QPIs) obtained by digital holographic microscopy. Particularly, the CM's nucleus section can precisely reflect the associated rhythmic beating pattern of the CM suitable for subsequent beating pattern characterization. In this paper, we describe an automated method to characterize single CMs by nucleus extraction from QPIs and subsequent beating pattern reconstruction and quantification. However, accurate CM's nucleus extraction from the QPIs is a challenging task due to the variations in shape, size, orientation, and lack of special geometry. To this end, we propose a novel fully convolutional neural network (FCN)-based network architecture for accurate CM's nucleus extraction using pixel classification technique and subsequent beating pattern characterization. Our experimental results show that the beating profile of multiple extracted single CMs is less noisy and more informative compared to the whole image slide. Applying this method allows CM characterization at the single-cell level. Consequently, several single CMs are extracted from the whole slide QPIs and multiple parameters regarding their beating profile of each isolated CM are efficiently measured.