Speeding Scientific Discovery with AI-Powered Microscopy | MathWorks

Left: The maximum image intensity projection of a three-dimensional stack of a butterfly ovary taken on a single-view lightsheet microscope. On right, a computationally cleaned version of the original image stack. Credit: Kyle Demarr and Sophia Kelly, MBL

User-Friendly Image Processing and Insights from Massive Lightsheet Data Sets

Optical microscopes are becoming increasingly important for interrogating biological samples, both live and fixed. Lightsheet technology lets scientists rapidly capture microscopic images and videos of living cells and organisms by imaging an entire cross-section in a single frame. This reduces laser photodamage to the sample, as compared with other optical imaging techniques, allowing for long-term imaging of sensitive samples such as cells, embryos, and tissues. 3D volumes are acquired through rapid serial scanning.

Researchers increasingly turn to this advanced imaging technique to visualize intricate structures within cells and track the development secrets hidden within embryonic origins. But these microscopes generate massive terabyte-sized volumes of complex imagery, creating bottlenecks in image analysis for scientists seeking insights.

A team building a new tool working with Dr. Abhishek Kumar, an investigator and Chan Zuckerberg Initiative Imaging Scientist at the Marine Biological Laboratory (MBL) in Woods Hole, Massachusetts, is guided by these concerns. They are building the new tool in MATLAB®.

The project, led by MBL Research Assistant William Ramos with help from MBL Imaging Specialist Anthony Mautino, is an end-to-end lightsheet microscopy workflow. From microscope control and image acquisition to reconstruction, visualization, analysis, and quantification, the tool helps researchers focus on their imagery instead of requiring them to learn to code. Read rest of the story here.

Source: Speeding Scientific Discovery with AI-Powered Microscopy | MathWorks