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

doi:10.1016/j.compbiomed.2020.104172

Cited by 38 publications

(25 citation statements)

References 31 publications

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“…iPSC produced in the SCF platform exhibited linear growth with only slight variations and more than 90% of cells expressed the pluripotency marker TRA-1-60 after expansion over 10 passages. A positive Epi-Pluri-Score additionally confirmed pluripotency, establishing the SCF as a platform for production and expansion of high-quality iPSC [40].…”

Section: Quality Of Stem Cellsmentioning

confidence: 82%

“…As the perception of confluence is different in between operators, variability is introduced into the production process. In an automated system, confluence is determined algorithmically based upon high-speed microscopy and therefore independent of the perception of the operator [40]. Reproducible cultivation of iPSC is further complicated by the fact that different iPSC lines typically exhibit differing growth patterns.…”

Section: Quality Of Stem Cellsmentioning

confidence: 99%

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Techno-Economic Analysis of Automated iPSC Production

et al. 2021

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Induced pluripotent stem cells (iPSC) open up the unique perspective of manufacturing cell products for drug development and regenerative medicine in tissue-, disease- and patient-specific forms. iPSC can be multiplied almost without restriction and differentiated into cell types of all organs. The basis for clinical use of iPSC is a high number of cells (approximately 7 × 107 cells per treatment), which must be produced cost-effectively while maintaining reproducible and high quality. Compared to manual cell production, the automation of cell production offers a unique chance of reliable reproducibility of cells in addition to cost reduction and increased throughput. StemCellFactory is a prototype for a fully automated production of iPSC. However, in addition to the already tested functionality of the system, it must be shown that this automation brings necessary economic advantages. This paper presents that fully automated stem cell production offers economic advantages in addition to increased throughput and better quality. First, biological and technological basics for a fully automated production of iPSC are presented. Second, the basics for profitability calculation are presented. Third, profitability of both manual and automated production are calculated. Finally, different scenarios effecting the profitability of manual and automated production are compared.

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Section: Quality Of Stem Cellsmentioning

confidence: 82%

Section: Quality Of Stem Cellsmentioning

confidence: 99%

Techno-Economic Analysis of Automated iPSC Production

et al. 2021

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“…As the diagnostic criteria for cervical cytology are mainly based on nuclear and cytoplasm abnormalities, accurate segmentation is a prerequisite for screening solutions (44)(45)(46). Studies on the application of AI in cell segmentation have been carried out continuously and have shown good results in the segmentation of hepatoma cells (47), human metaphase II oocytes (43), and pluripotent stem cells (48). It has also been introduced for the automatic segmentation of cervical cells, and good results have been reported (49).…”

Section: Segmentation Of Cervical Cellsmentioning

confidence: 99%

Artificial Intelligence in Cervical Cancer Screening and Diagnosis

Hou

Shen

Zhou³

et al. 2022

Front. Oncol.

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Cervical cancer remains a leading cause of cancer death in women, seriously threatening their physical and mental health. It is an easily preventable cancer with early screening and diagnosis. Although technical advancements have significantly improved the early diagnosis of cervical cancer, accurate diagnosis remains difficult owing to various factors. In recent years, artificial intelligence (AI)-based medical diagnostic applications have been on the rise and have excellent applicability in the screening and diagnosis of cervical cancer. Their benefits include reduced time consumption, reduced need for professional and technical personnel, and no bias owing to subjective factors. We, thus, aimed to discuss how AI can be used in cervical cancer screening and diagnosis, particularly to improve the accuracy of early diagnosis. The application and challenges of using AI in the diagnosis and treatment of cervical cancer are also discussed.

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“…During the purifica on process iPSC clones with high pluripotency and stemness are selected based on either gene expression profiling data, such as RNA-seq, or cell morphology as evaluated by visual inspec on or image analysis (6)(7)(8)(9)(10)(11)(12) . However, both of these approaches have major limita ons.…”

Section: Introduc Onmentioning

confidence: 99%

Predicting gene expression from cell morphology in human induced pluripotent stem cells

Wakui

Negishi

Murakami

et al. 2022

Preprint

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Purification is essential before differentiating human induced pluripotent stem cells (hiPSCs) into cells that fully express particular differentiation marker genes. High-quality iPSC clones are typically purified through gene expression profiling or visual inspection of the cell morphology; however, the relationship between the two methods remains unclear. We investigated the relationship between gene expression levels and morphology by analyzing live-cell phase-contrast images and mRNA profiles collected during the purification process. We employed this data and an unsupervised image feature extraction method to build a model that predicts gene expression levels from morphology. As a benchmark, we confirmed that the method can predict the gene expression levels from tissue images for cancer genes, performing as well as state-of-the-art methods. We then applied the method to iPSCs and identified two genes that are well-predicted from cell morphology. Although strong batch effects resulting from the reprogramming process preclude the ability to use the same model to predict across batches, prediction within a reprogramming batch is sufficiently robust to provide a practical approach for estimating expression levels of a few genes and monitoring the purification process.

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

Cited by 38 publications

References 31 publications

Techno-Economic Analysis of Automated iPSC Production

Techno-Economic Analysis of Automated iPSC Production

Artificial Intelligence in Cervical Cancer Screening and Diagnosis

Predicting gene expression from cell morphology in human induced pluripotent stem cells

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