Automatic cell counting
T1 cells in matrigel are detected (red dots) and their number is counted despite ovelapping of cells.
T1 cells in matrigel are detected (red dots) and their number is counted despite ovelapping of cells.
Azafan staining and subsequent tissue segmentation.
Detection of infected by influenza virus cells (brown) and healthy cells (red)
Grouping into clusters of cells affected by a virus (blue) and healthy cells (red)
Parasite (acida) is automatically detected and distinguished from bubbles and dirt
Tumor cells and blood vessels are segmented based on their staining.
Scanning electron microscope (SEM) reveals how black silicon (BSi) flexible nanopillars cluster together in vacuum. Neural network has detected individual (non-clustered) pillars (red) and separated from clusters of pillars (blue). Coordinates of pillars and pair correlation function of pillar positions was calculated.
Each client has a personalized quote and work plan. The algorithms may be tuned and ajusted to the needs of the clients.
The prices are determined case-by-case taking into account the exact requirements of a client and complexity of the required work.
Our servers are ready for computationally intensive jobs to process big data taking advantage of power of modern GPUs and artificial intelligence.
We are open for unexplored scientific problems that can be solved by massive analysis of images using machine learning techniques.
Episenses,
Fundació URV,
Av. Paisos Catalans 18
43007 Tarragona, Spain
e-mail: [email protected]
tel: +34 6 44448558