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Laboratory automation is a key driver in biotechnology and an enabler for powerful new technologies and applications. In particular, in the field of personalized therapies, automation in research and production is a prerequisite for achieving cost efficiency and broad availability of tailored treatments. For this reason, we present the StemCellDiscovery, a fully automated robotic laboratory for the cultivation of human mesenchymal stem cells (hMSCs) in small scale and in parallel. While the system can handle different kinds of adherent cells, here, we focus on the cultivation of adipose-derived hMSCs. The StemCellDiscovery provides an in-line visual quality control for automated confluence estimation, which is realized by combining high-speed microscopy with deep learning-based image processing. We demonstrate the feasibility of the algorithm to detect hMSCs in culture at different densities and calculate confluences based on the resulting image. Furthermore, we show that the StemCellDiscovery is capable of expanding adipose-derived hMSCs in a fully automated manner using the confluence estimation algorithm. In order to estimate the system capacity under high-throughput conditions, we modeled the production environment in a simulation software. The simulations of the production process indicate that the robotic laboratory is capable of handling more than 95 cell culture plates per day.

Graphical AbstractClonal iPS cell lines and embryoid bodies (EB) of defined size are obtained by cell cluster sorting in an automated platform.

A growing world population requires sufficient food to sustain itself. Therefore, increasingly more resources are required to produce the food. Insects are a viable food and feed alternative since their production requires only a fraction of the resources that conventional livestock needs. For the efficient production of insects, automation technology is needed. An automatic monitoring of the insects’ growth ensures stable production processes and a high product quality. The use of a camera with image processing using neural networks makes it possible to detect insects, measure their features such as shape and colour and enables to derive their age, size, and health. In this paper, instance segmentation using mask scoring regional convolutional neural network (Mask Scoring R-CNN) shows good results in detecting house crickets (Acheta domesticus). A dataset is created consisting of six images, showing 1,022 insect instances, of a real-world cricket production facility to train and test the algorithm. Furthermore, image augmentation by cropping, flipping and rotating is applied to the set to solve the problem of limited data. By combining the augmentations, 288 different trainings are compared to find the best augmentation strategy. The evaluation of the algorithm uses two variations of the F1-score: one variation to estimate the capabilities of producing qualitative segmentation masks and another to estimate the detection capabilities. For the estimation of the detection capabilities, a rule termed ‘centre over ground truth’ is developed. The results show that the presented method is suitable for monitoring a cricket production facility with a recall of 76.6% and a precision of 96.2%.