Abstract The Cell Tracking Challenge is an ongoing benchmarking initiative that has
become a reference in cell segmentation and tracking algorithm development. Here, we …

Recently, human monkeypox outbreaks have been reported in many countries. According to
the reports and studies, quick determination and isolation of infected people are essential to …

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 …

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 …

Abstract Multiple Object Tracking (MOT) is a rapidly develo** research field that targets
precise and reliable tracking of objects. Unfortunately, most available MOT datasets typically …

Cell tracking and motility analysis are essential for understanding multicellular processes,
automated quantification in biomedical experiments, and medical diagnosis and treatment …

Microscopy cell segmentation is a crucial step in biological image analysis and a
challenging task. In recent years, deep learning has been widely used to tackle this task …

Hypoxic ischemic encephalopathy (HIE) is a brain injury occurring in approximately 1-
5/1000 term-born neonates. Accurate segmentation of HIE lesions in brain MRI is crucial for …

Model-based deep learning has the potential to significantly reduce the size of deep
architectures while matching the competitive performance of much deeper and wider …

Semantic segmentation of cells is the entry point to other areas of cell analysis such as
instance segmentation, cell detection, Mitosis detection, and cell tracking. This paper …