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Automated single cardiomyocyte characterization by nucleus extraction from dynamic holographic images using a fully convolutional neural network

Title
Automated single cardiomyocyte characterization by nucleus extraction from dynamic holographic images using a fully convolutional neural network
Authors
Ahmadzadeh, EzatJaferzadeh, KeyvanShin, SeokjooMoon, Inkyu
DGIST Authors
Ahmadzadeh, Ezat; Jaferzadeh, Keyvan; Shin, Seokjoo; Moon, Inkyu
Issue Date
2020-03
Citation
Biomedical Optics Express, 11(3), 1501-1516
Type
Article
Article Type
Article
Keywords
NUMERICAL RECONSTRUCTIONLIVING CELLSVISUALIZATIONMICROSCOPYCONTRASTCLAMP
ISSN
2156-7085
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. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.
URI
http://hdl.handle.net/20.500.11750/11615
DOI
10.1364/BOE.385218
Publisher
The Optical Society
Related Researcher
  • Author Moon, Inkyu Intelligent Imaging and Vision Systems Laboratory
  • Research Interests
Files:
Collection:
Department of Robotics EngineeringIntelligent Imaging and Vision Systems Laboratory1. Journal Articles


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