Deep-learning-based multi-class segmentation for automated, non-invasive routine assessment of human pluripotent stem cell culture status
Computers in Biology and Medicine, vol. 129, pp. 104172
Abstract
Human induced pluripotent stem cells (hiPSCs) are capable of differentiating into a variety of human tissue cells. They offer new opportunities for personalized medicine and drug screening. This requires large quantities of high quality hiPSCs, obtainable only via automated cultivation. One of the major requirements of an automated cultivation is a regular, non-invasive analysis of the cell condition, e.g. by whole-well microscopy. However, despite the urgency of this requirement, there are currently no automatic, image-processing-based solutions for multi-class routine quantification of this nature. This paper describes a method to fully automate the cell state recognition based on phase contrast microscopy and deep-learning. This approach can be used for in process control during an automated hiPSC cultivation. The U-Net based algorithm is capable of segmenting important parameters of hiPSC colony formation and can discriminate between the classes hiPSC colony, single cells, differentiated cells and dead cells. The model achieves more accurate results for the classes hiPSC colonies, differentiated cells, single hiPSCs and dead cells than visual estimation by a skilled expert. Furthermore, parameters for each hiPSC colony are derived directly from the classification result such as roundness, size, center of gravity and inclusions of other cells. These parameters provide localized information about the cell state and enable well based treatment of the cell culture in automated processes. Thus, the model can be exploited for routine, non-invasive image analysis during an automated hiPSC cultivation. This facilitates the generation of high quality hiPSC derived products for biomedical purposes.
Authors 12
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Fraunhofer Institute for Production Technology IPT
Affiliation as printed
Fraunhofer Institute for Production Technology IPT, Aachen, Germany. Electronic address: tobias.piotrowski@ipt.fraunhofer.de
Fraunhofer Institute for Production Technology IPT, Aachen, Germany
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Fraunhofer Institute for Production Technology IPT
Affiliation as printed
Fraunhofer Institute for Production Technology IPT, Aachen, Germany
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University of Bonn · University Hospital Bonn · Life & Brain (Germany)
Affiliation as printed
Life & Brain GmbH, Cellomics Unit, Bonn, Germany; Institute of Reconstructive Neurobiology, University of Bonn Medical Faculty &University Hospital Bonn, Bonn, Germany
Institute of Reconstructive Neurobiology, University of Bonn Medical Faculty &University Hospital Bonn, Bonn, Germany
Life & Brain GmbH, Cellomics Unit, Bonn, Germany
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Fraunhofer Institute for Production Technology IPT
Affiliation as printed
Fraunhofer Institute for Production Technology IPT, Aachen, Germany
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Fraunhofer Institute for Production Technology IPT
Affiliation as printed
Fraunhofer Institute for Production Technology IPT, Aachen, Germany
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Fraunhofer Institute for Production Technology IPT
Affiliation as printed
Fraunhofer Institute for Production Technology IPT, Aachen, Germany
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Fraunhofer Institute for Production Technology IPT
Affiliation as printed
Fraunhofer Institute for Production Technology IPT, Aachen, Germany
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Affiliation as printed
Life & Brain GmbH, Cellomics Unit, Bonn, Germany
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Affiliation as printed
Life & Brain GmbH, Cellomics Unit, Bonn, Germany
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University of Bonn · University Hospital Bonn · Life & Brain (Germany)
Affiliation as printed
Life & Brain GmbH, Cellomics Unit, Bonn, Germany; Institute of Reconstructive Neurobiology, University of Bonn Medical Faculty &University Hospital Bonn, Bonn, Germany
Institute of Reconstructive Neurobiology, University of Bonn Medical Faculty &University Hospital Bonn, Bonn, Germany
Life & Brain GmbH, Cellomics Unit, Bonn, Germany
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Robert Heinrich Schmitt Aachen Fraunhofer Institute for Production Technology IPT Laboratory for Machine Tools and Production (WZL) Laboratory for Machine Tools and Production (WZL)
Fraunhofer Institute for Production Technology IPT · RWTH Aachen University
Affiliation as printed
Fraunhofer Institute for Production Technology IPT, Aachen, Germany; Laboratory for Machine Tools and Production (WZL), RWTH Aachen, Germany
Fraunhofer Institute for Production Technology IPT, Aachen, Germany
Laboratory for Machine Tools and Production (WZL), RWTH Aachen, Germany
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Affiliation as printed
Department of Medical Informatics, RWTH Aachen University, Germany
Cited by 50 stored of 50
Cited by patents worldwide 1 (Lens.org)
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IMAGING-BASED SYSTEM FOR MONITORING QUALITY OF CELLS IN CULTUREEP4562597A4 2025-12-24 Pending