Nucleus segmentation: towards automated solutions

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 cells, extracting features or classifying cellular phenotypes strongly depends on segmentation accuracy. Worldwide competitions have been held, aiming to improve segmentation, and recent years have definitely brought significant improvements: large annotated datasets are now freely available, several 2D segmentation strategies have been extended to 3D, and deep learning approaches have increased accuracy. However, even today, no generally accepted solution and benchmarking platform exist. We review the most recent single-cell segmentation tools, and provide an interactive method browser to select the most appropriate solution. Nucleus segmentation is one of the first steps of many microscopy image analysis pipelines.Several large-scale competitions have yielded annotated datasets that are available for training and testing specific methods.The 2D segmentation strategies cover a diverse range of image modalities; some of these are also available for 3D datasets.Simple cases of segmentation, especially for 2D, are straightforward, while in more challenging cases improved accuracy has been achieved recently.Deep learning has established a new level of image analysis, but the lack of uniform evaluation strategies makes quantitative comparison and relative performance determination highly challenging.