Recent advances in computer vision and machine learning underpin a collection of
algorithms with an impressive ability to decipher the content of images. These deep learning …

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 …

Many biological applications require the segmentation of cell bodies, membranes and nuclei
from microscopy images. Deep learning has enabled great progress on this problem, but …

Segmenting the nuclei of cells in microscopy images is often the first step in the quantitative
analysis of imaging data for biological and biomedical applications. Many bioimage analysis …

Recent advancements in deep learning have revolutionized the way microscopy images of
cells are processed. Deep learning network architectures have a large number of …

The accurate segmentation and tracking of cells in microscopy image sequences is an
important task in biomedical research, eg, for studying the development of tissues, organs or …

Purpose Wood comprises different cell types, such as fibers, tracheids and vessels, defining
its properties. Studying cells' shape, size, and arrangement in microscopy images is crucial …

Nuclear atypia scoring (NAS), forms a significant factor in determining individualized
treatment plans and also for the prognosis of the disease. Automation of cancer grading …

Cell images, which have been widely used in biomedical research and drug discovery,
contain a great deal of valuable information that encodes how cells respond to external …

AnnotatorJ combines single-cell identification with deep learning (DL) and manual
annotation. Cellular analysis quality depends on accurate and reliable detection and …