Regardless of the challenges you encounter in your cell biology and cancer research, our powerful software brings you one step closer to obtaining reliable and reproducible results. Create automated AI-driven pipelines for a wide range of image analysis tasks.
Quantify the level of DNA damage in DAPI stained cells by measurement of signal intensities of foci in 2 channels
Phenotypic characterization of cells based on cell morphology and intensities of multiple fluorescent markers in the nucleus, cytoplasm or membrane
Learn about automated, scalable AI Image Analysis for Volume Electron Microscopy to analyse mitochondria in the nucleus and the nuclear membrane and pores.
Finding cell boundaries based on membrane-localized contrast is made many times easier by new algorithms that enhance membranes in 3D and an operator designed to segment cells in 3D.
The segment tracker operator used for connecting moving objects in time, that is finding relationship of objects during a time course.
The Blob Finder analysis operator is ideal for segmenting cells or any other type of rounded cell organelles in a noisy image in an easy-to-use 3-step process.
Identifying objects in images acquired by electron microscopy (EM) can be challenging. Since contrast and intensity distributuion in EM images is generally low, simple segmentation algorithms which are based on intensity thresholds or contrast detection often fail with such datasets.
Tracking cells or subcellular particles in microscopic data can be challenging. The Tracking Module allows analysis of the movement of small or large objects over time in both 2D or 3D multichannel image sets of any size.
High-Content Screening (HCS) has played a significant role in infectious disease research and drug discovery to date and can be a strong tool in the age of COVID-19 and beyond.