Saturday, November 23, 2024

ZEISS arivis software enhances custom microscopy image analysis

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ZEISS unveiled arivis Pro version 4.2, empowering researchers with unprecedented flexibility to tailor microscopy image analysis to their unique needs. This major release introduces advanced AI-powered segmentation tools, enhanced 3D analysis capabilities, and seamless handling of massive datasets – a universal solution optimized for any imaging workflow.

The power of customization

“Our goal with arivis Pro 4.2 is to put the power of customization directly into the user’s hands,” says Dr. Sreenivas Bhattiprolu, Head of Digital Solutions at ZEISS. “Whether working with small datasets or incredibly large 3D volumes, researchers can now leverage the most appropriate tools for their specific analysis requirements.”

Advanced AI-powered segmentation tools

A cornerstone of the new version is the integration of cutting-edge instance segmentation models powered by deep learning. Trained on the ZEISS arivis Cloud, these models enable users to precisely segment individual objects within images using AI – without any coding expertise required. Additionally, pre-trained models from the open-source Cellpose library enable users to segment cells and nuclei in most images effortlessly.

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“In microscopy, no two datasets are alike,” explains Dr. Bhattiprolu. “Some demand traditional segmentation methods, while others benefit from AI-driven approaches. With arivis Pro 4.2, we empower researchers to choose the ideal techniques for each unique scenario.”

Enhanced 3D analysis capabilities and seamless handling of massive datasets

As researchers increasingly adopt 3D imaging methods to gain more comprehensive insights, especially in areas like drug discovery, the need for powerful 3D analysis tools continues to grow. With arivis Pro 4.2, ZEISS delivers a robust solution tailored for these evolving demands. The new version allows users to seamlessly load, visualize, and extract rich insights from massive 3D datasets containing a vast number of objects, enabling unprecedented exploration of complex 3D volumes at scale.

Source: PRNewswire

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