syGlass Summer 2025 Release: v2.4.0

syGlass is a multipurpose tool, with applications in a wide range of fields that rely on the visualization and analysis of 3D image data. Today, automated annotation is a shared interest of researchers in nearly all of these fields.

Our summer release of syGlass v2.4.0 brings with it new auto-annotation techniques that can be applied to a wide variety of workflows, alongside a slew of other improvements. Learn more about the biggest changes below, check out the full changelog, and don’t hesitate to send us a message for any assistance you need in getting started.

The New Auto-Tracking Tool

Tracking objects through time in complex, 4D datasets has long been a core feature of syGlass. In this update, the ability to do so in automated fashion is introduced for the first time.

The new auto-tracking tool, for which a full video tutorial is available below, allows you to define bounding boxes around objects in the time series that you’d like to have tracked. These bounding boxes are defined in the first frame in which the object appears (or the first frame in which you’re interested in tracking the object) and a centroid for the object in each subsequent frame is then generated.

A brief tutorial on automatically tracking objects in time series image volumes using the new Auto Track Tool in syGlass v2.4.0.

Proofreading and editing the results, of course, can all be done in the virtual environment using the existing tracking tools, making syGlass a robust solution for any combination of automated and manual tracking techniques.

Interfacing with Cellpose-SAM for 3D Cell Segmentation

Cellpose-SAM has made a big splash in the world of cell segmentation recently, boasting “superhuman” generalization that removes the need for some pre-processing steps for noisy images, eliminates a size estimation pass, and takes advantage of the Segment Anything Model (SAM) for key steps of the process.

Cellpose-SAM segmentation of cells in a zebrafish eye, visualized in syGlass.

syGlass v2.4.0 now supports interfacing with Cellpose-SAM, allowing segmentation to be performed on a specified portion of your image via the Auto-Segment tool. By providing input image data in chunks and stitching the results together automatically, the memory constraints that typically make large-scale auto-segmentation efforts impractical on consumer-grade machines can be avoided. The results can then be quickly visualized and edited in syGlass, taking advantage of the existing suite of segmentation tools and the power of VR visualization for error detection.

Visualization Menu Improvements

No matter what your reasons are for using syGlass, you certainly interact with the visualization settings menu, tweaking the way your images are rendered in the software. This update brings a few new features to this menu.

A demonstration of the effects of brightness, contrast, window, and threshold on the transfer function that is newly drawn on the image histogram when adjusting visualization settings in syGlass v2.4.0.

What used to be the “Performance” slider is now more intuitively named “Quality,” and has been inverted so that image quality increases at the expense of performance as the slider is dragged to the right. To remind you of this change, the right end of the slider has been colored in a red gradient, to indicate that settings near the top end of the slider could push some GPUs beyond the limits of good performance.

A transfer function is also now drawn over the histogram above each slider, showing the relationship between the original image values and the corresponding visualized intensities.

NIfTI File Support

NIfTI, an acronym for Neuroimaging Informatics Technology Initiative, is a common format for the storage of 3D medical images, particularly in neuroimaging. In previous versions of syGlass, these images had to be converted to DICOM, TIFF, or some other supported image format. syGlass v2.4.0 introduces NIfTI support for the first time, allowing most files of this type—bearing either .nii or .nii.gz file extensions—to be converted into syGlass projects seamlessly.

… and Much More

There are lots of other exciting changes in v2.4.0: updates to the label tool, better control over foveated rendering and volume settings, new options for scale bar overlays, and more. Read about it all in the full changelog, and then get in touch with us if you’d like to give it a try.

We’re committed to delivering new value in the syGlass platform every quarter, and this fall will be no exception: stay tuned for what’s next in syGlass v2.5.0.

Zebrafish eye dataset provided by the Cheng Lab at Penn State University.