Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …

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 (DL) methods are powerful analytical tools for microscopy and can
outperform conventional image processing pipelines. Despite the enthusiasm and …

Despite the availabilty of imaging-based and mass-spectrometry-based methods for spatial
proteomics, a key challenge remains connecting images with single-cell-resolution protein …

The cellular morphology and sub-cellular spatial structure critically influence the function of
microbial cells. Similarly, the spatial arrangement of genotypes and phenotypes in microbial …

In computational pathology, automatic nuclei instance segmentation plays an essential role
in whole slide image analysis. While many computerized approaches have been proposed …

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

The primary step in tissue cytometry is the automated distinction of individual cells
(segmentation). Since cell borders are seldom labeled, cells are generally segmented by …

Label-free, live cell recognition (ie instance segmentation) and tracking using computer
vision-aided recognition can be a powerful tool that rapidly generates multi-modal readouts …

In many research areas, scientific progress is accelerated by multidisciplinary access to
image data and their interdisciplinary annotation. However, kee** track of these …