Waibel, D. J. E. | Search bioRxiv

bioRxiv2022

Label-free imaging of 3D pluripotent stem cell differentiation dynamics on chip

The dynamic chemical and architectural microenvironments of 3D stem cell cultures can be controlled by integration into a microfluidic chip. Massive parallelized 3D stem cell cultures for engineering in vitro human cell types require new imaging methods with high time and spatial resolution to fully exploit technological advances in cell culture. Here, we introduce a label-free deep learning method called Bright2Nuc to predict in silico nuclear staining in 3D from bright-field images obtained using traditional confocal microscopy. Bright2Nuc was trained and applied to several hundred 3D human induced pluripotent stem cell cultures differentiating towards definitive endoderm on a microfluidic platform. Combined with existing image analysis tools, Bright2Nuc segmented individual nuclei from bright-field images, quantified their morphological properties, predicted stem cell differentiation state, and tracked the cells over time. Our methods are available in an open-source pipeline that enables researchers to upscale 3D cell phenotyping in stem cell culture.

bioinformatics↗

bioRxiv2020

InstantDL - An easy-to-use deep learning pipeline for image segmentation and classification

MotivationDeep learning contributes to uncovering and understanding molecular and cellular processes with highly performant image computing algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate, consistent and fast data processing. However, published algorithms mostly solve only one specific problem and they often require expert skills and a considerable computer science and machine learning background for application. ResultsWe have thus developed a deep learning pipeline called InstantDL for four common image processing tasks: semantic segmentation, instance segmentation, pixel-wise regression and classification. InstantDL enables experts and non-experts to apply state-of-the-art deep learning algorithms to biomedical image data with minimal effort. To make the pipeline robust, we have automated and standardized workflows and extensively tested it in different scenarios. Moreover, it allows to assess the uncertainty of predictions. We have benchmarked InstantDL on seven publicly available datasets achieving competitive performance without any parameter tuning. For customization of the pipeline to specific tasks, all code is easily accessible. Availability and ImplementationInstantDL is available under the terms of MIT licence. It can be found on GitHub: https://github.com/marrlab/InstantDL Contactcarsten.marr@helmholtz-muenchen.de

bioinformatics↗