Methods for profiling RNA and protein expression in a spatially resolved manner are rapidly
evolving, making it possible to comprehensively characterize cells and tissues in health and …

Spatial omics technologies enable a deeper understanding of cellular organizations and
interactions within a tissue of interest. These assays can identify specific compartments or …

Spatial omics data are advancing the study of tissue organization and cellular
communication at an unprecedented scale. Flexible tools are required to store, integrate and …

Quantitative evaluation of image segmentation algorithms is crucial in the field of bioimage
analysis. The most common assessment scores, however, are often misinterpreted and …

Multiplexed imaging and spatial transcriptomics enable highly resolved spatial
characterization of cellular phenotypes, but still largely depend on laborious manual …

This work demonstrates and guides how to use a range of state-of-the-art artificial neural-
networks to analyse bacterial microscopy images using the recently developed …

Single nucleus segmentation is a frequent challenge of microscopy image processing, since
it is the first step of many quantitative data analysis pipelines. The quality of tracking single …

Deep learning has transformed the way large and complex image datasets can be
processed, reshaping what is possible in bioimage analysis. As the complexity and size of …

Single-cell methods are beginning to reveal the intrinsic heterogeneity in cell populations,
arising from the interplay of deterministic and stochastic processes. However, it remains …

The growth of artificial intelligence (AI) has led to an increase in the adoption of computer
vision and deep learning (DL) techniques for the evaluation of microscopy images and …