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

Massive, parallelized 3D stem cell cultures for engineering human cell types require imaging methods with high time and spatial resolution to fully exploit technological advances in cell culture technologies. Here, we introduce a large-scale integrated microfluidic chip platform for automated 3D stem cell differentiation. To fully enable dynamic high-content imaging on the chip platform, we developed a label-free deep learning method called Bright2Nuc to predict nuclear staining in 3D from confocal microscopy bright-field images.

InstantDL: an easy-to-use deep learning pipeline for image segmentation and classification.

Background: Deep learning contributes to uncovering molecular and cellular processes with highly performant algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate and fast image data processing. However, published algorithms mostly solve only one specific problem and they typically require a considerable coding effort and machine learning background for their application.