Given the recent success of artificial intelligence (AI) in computer vision applications, many
pathologists anticipate that AI will be able to assist them in a variety of digital pathology …

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images.
Existing cell segmentation methods are often tailored to specific modalities or require …

Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images
are important clinical tasks and crucial for a wide range of applications. However, it is a …

In common medical procedures, the time-consuming and expensive nature of obtaining test
results plagues doctors and patients. Digital pathology research allows using computational …

The recent surge in performance for image analysis of digitised pathology slides can largely
be attributed to the advances in deep learning. Deep models can be used to initially localise …

Over the past few years, the rapid development of deep learning technologies for computer
vision has significantly improved the performance of medical image segmentation …

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

Instance segmentation and classification of nuclei is an impor-tant task in computational
pathology. We show that StarDist, a deep learning nuclei segmentation method originally …

Cell localization in medical image analysis is a challenging task due to the significant
variation in cell shape, size and color. Existing localization methods continue to tackle these …

Nuclei segmentation is a fundamental prerequisite in the digital pathology workflow. The
development of automated methods for nuclei segmentation enables quantitative analysis of …