Imaris launches version 9.9 with machine learning and open source connections
The addition of machine learning broadens the diversity of images for analysis, enabling electron microscopy segmentation
18 Mar 2022
Imaris, an Oxford Instruments brand, has released Imaris 9.9, the latest version of its market-leading microscopy image analysis software.
Imaris 9.9 is the most intelligent and flexible version ever, featuring segmentation with machine learning, as well as open source connections.
The addition of machine learning pixel classification, with an intuitive and interactive training mode, broadens the diversity of images for analysis as it enables electron microscopy segmentation and shape recognition.
Imaris 9.9 adds a new pixel classification segmentation method powered by Labkit, an open source Fiji plug-in. This work builds upon the ImgLib2-Imaris Bridge work by Tobias Pietzsch and provides a seamless workflow from Imaris to Labkit and back to Imaris.
To facilitate the connection between open source software packages and Imaris, the 9.9 version provides users with the ability to directly import label images or position data as Surfaces or Spots, respectively. Once imported, these components are compatible with other Imaris tools as if the results were created natively. These tools include statistics reporting, spatial analysis, and stunning animations and snapshots.
Meredith Price, the Imaris Business Manager, commented: “We listened to our customers as they told us they wanted that software works effortlessly with open source tools that are important for their specific research, and have facilitated that connectivity with our latest release. The addition of machine learning pixel classification has made our software, which was already incredibly intelligent and intuitive, even better.
“The latest release will enable researchers to save time and become more productive, without compromising results.”
Imaris’ goal is to bring you the most comprehensive visualization and analysis software for 3D/4D and time-lapse microscopic images.
