IN Carta SINAP
Easy, accurate results using deep learning-based trainable segmentationRequest a demo
Why is segmentation so important?
Segmentation is the first step in converting image pixels into numerical data. Researchers use image segmentation to detect and categorize specific areas of interest, from organelles to whole cells.
Results from segmentation are used downstream to extract quantitative measurements and draw key conclusions. Because errors will propagate in subsequent steps, precise and accurate segmentation is critical in driving robust analyses.
How does IN Carta SINAP work?
The IN Carta SINAP module uses deep learning to make segmentation easier, faster, and trainable. Take away some of the heavy lifting by starting with a pre-trained model, then test and fine tune as needed. If the model does not perform well, go back and retrain to solve a new problem.
With In Carta SINAP, at no point in the process will users get stuck and there are no limitations to what can be segmented.
Reliable, flexible, accessible segmentation
IN Carta SINAP — where segmentation is not a problem.
- Works with any type of data, including brightfield
- Highly accurate with both low signal-to-noise ratios and high-density images
- Able to cope with high phenotypic variability within an assay
- Easy to learn and use, without needing an image analysis or programming background
Most importantly, IN Carta SINAP is built to solve any type of segmentation problem.