Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Deep-learning-based multi-class segmentation for automated, non-invasive routine assessment of human pluripotent stem cell culture status

: Piotrowski, Tobias; Rippel, Oliver; Elanzew, Andreas; Nießing, Bastian; Stucken, Sebastian; Jung, Sven; König, Niels; Haupt, Simone; Stappert, Laura; Brüstle, Oliver; Schmitt, Robert; Jonas, Stephan

Fulltext ()

Computers in biology and medicine 129 (2021), Art. 104172, 10 pp.
ISSN: 0010-4825
Ministerium für Kultur und Wissenschaft Nordrhein-Westfalen
EFRE; 005-1007-0021; Stemcellfactory I
Ministerium für Kultur und Wissenschaft Nordrhein-Westfalen
EFRE; 005-1403-0102; Stemcellfactory II
Ministerium für Kultur und Wissenschaft Nordrhein-Westfalen
EFRE; EFRE-0800978; Stemcellfactory III
Journal Article, Electronic Publication
Fraunhofer IPT ()
human induced pluripotent stemcells (hiPSC); deep learning; cell analysis; routine parameter calculation; automated cell culture; multi class segmentation; microscopy

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 highquality 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 auto mated 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.