Quantitative image analysis is a critical step in many materials science, industrial, and quality assurance applications. Conventional methods that depend on brightness or color can miss critical information or targets in samples—especially when performed by inexperienced users. Since image quality and contrast varies with the sample, image segmentation using classical thresholding methods lacks reproducibility and repeatability.
Join us to learn how OLYMPUS Stream™ TruAI solution offers a more accurate segmentation approach using deep-learning technology for highly reproducible and robust analyses. With an intuitive user interface, even inexperienced operators can efficiently label images and easily train robust models with excellent generalization properties. A pre-trained network can be applied to future analyses for a similar application.
