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Olympus: deep learning streamlines industrial image analysis for material inspections

Published by , Editorial Assistant
World Pipelines,

OLYMPUS Stream™ image analysis software now leverages the power of artificial intelligence to bring next-generation image segmentation to industrial microscope inspections. Software version 2.5 adds Olympus’ TruAI™ deep-learning technology, enabling users to train neural networks to automatically segment and classify objects in microscope images for a range of material inspections. A trained network can be applied to future analyses for a similar application to maximise efficiency.

Accurate image segmentation

Image analysis is a critical part of many material science, industrial and quality assurance applications. However, image segmentation using conventional thresholding methods that depend on HSV or RGB colour spaces can miss critical information or targets in samples. Olympus’ TruAI technology offers more accurate segmentation based on deep learning for a highly reproducible and robust analysis.

Easily train and manage neural networks

With the TruAI solution, users can easily train robust neural networks. An easy-to-use interface lets users efficiently label images and run trainings in batches. Networks can be configured with many input channels, trained to identify up to 16 classes, and imported or exported. The solution also offers options to review and edit training details.

Customised user workflows

The software update also gives all users access to Olympus’ workflow customisation services. This team designs tailor-made OLYMPUS Stream workflows to address user-specific application scenarios, challenges, and goals.

Update to OLYMPUS Stream software version 2.5

OLYMPUS Stream v. 2.4 customers may use their existing license for a free update to software version 2.5. For more information about OLYMPUS Stream image analysis software, visit

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