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Create New Options for Live Cell Imaging

With THUNDER and Aivia

3D reconstruction of an isolated human islet THUNDER-Imager-3D-Cell-Culture_isolated-human-islet.jpg

One of the major challenges when imaging thick live samples is balancing image quality with tissue integrity. Low incident light doses over long periods of image acquisition can lead to low signal levels, causing low contrast images and difficulties with segmentation and analysis. The problem only gets worse when high dosage imaging or high temporal resolution imaging techniques are required to boost signal intensity. So often, the question is - do I image hard and fast, hoping for a one-take shot, or will it cause too much photobleaching of a sample or cell death?

However, new AI systems are making this challenge a thing of the past. Two new technologies – THUNDER and Aivia – can be used to return high-quality images and analysis of widefield images without compromising the longevity of samples. THUNDER uses computational methods to significantly increase image contrast during imaging with low light dosages. In combination, Aivia’s machine learning-based object detection segments and accurately analyzes these higher-contrast images to produce reliable reconstructions of highly complex samples.

High contrast images in real-time

When using conventional methods, imaging with a low light dosage can produce low-quality images with low contrast levels, causing blurred and low-resolution images. This is especially true in thick samples, where out-of-focus blur masks the complexity of the biological structures. THUNDER’s computational clearing approaches are overcoming this challenge by reducing the background on an image in real time, producing crisper images with higher contrast, making even thick samples accessible for high-powered segmentation and analysis.

The real benefit of THUNDER imaging systems is that high image quality does not come at the cost of high light dosage. Gentle widefield imaging avoids damage to samples and ensures their longevity, allowing for multiple scans of large, thick samples over long periods of time. This low-cost high-reward approach maximizes data collected from each experiment, allowing more robust and rapid image analysis of single cells, tissues,
and whole organisms.

Improving image analysis with AI

Aivia is designed to make segmentation and analysis powerful, simple, and reproducible. It uses AI-driven tools like object detection and interpretation to segment and analyze highly complex images. Aivia’s suit of image visualization and analysis packages are designed to automate complex analysis in huge datasets in the range of hundreds of GBs. For example, Aivia can detect and track 2D and 3D objects (like cells, nuclei, organelles, particles, etc.) in time-lapse, in 2-5D reconstructed stacks, and analyze a wide range of morphology, intensity, and motion measurements.

However, accurate segmentation and powerful analysis require high-quality images, which is where combining Aivia and THUNDER comes in. Results are especially powerful when Aivia is combined with computationally cleared THUNDER images, as the high contrast images allow for more rapid and accurate segmentation and analysis. Aivia’s machine learning-based analysis allows lower exposure images to be taken, reducing photo stress on cells.

Overall, combining THUNDER´s high contrast images with Aivia’s AI-driven segmentation and analysis maximizes the physiological relevance of camera-based imaging. The automated, AI-driven package also means that analysis lacks subjective bias, meaning that data can be reproducible across experiments and experimenters.


The use of state-of-the-art AI systems is pushing image analysis into a new generation. Challenges like the conflict between imaging power and sample integrity are being overcome with THUNDER’s computational systems which push image quality and contrast at low light levels. Moreover, AI-based segmentation and analysis with Aivia is enabling researchers to perform ever more powerful and reproducible analysis on high-quality images.

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