What's new

New features and improvements in NeuroInfo version 2026

Images now open at the center of the stack by default. There is a new Preference setting to modify this behavior.
Image adjustments set using the histogram are preserved when exporting a stack from the Serial Section Assembler workflow.
New capability to open OME-Zarr images in NeuroInfo software.
NeuroInfo now includes a built-in Python REPL (read-eval-print loop), giving you a live Python environment with direct programmatic access to your image data. MBF Bioscience- supported image data can be manipulated in the REPL itself, or via custom scripts, using the new MBF Python module.

Updated and improved developmental mouse atlases (devCCF and epDev) are now available on the MBF BioscienceDownload Center
You can now synchronize related experimental and atlas views during registration
More precise control of traced-structure clipping during registration is now enabled. Use the new Thickness slider to indicate the z depth of modeled structures in images, such as automatically detected cells and mapped brain regions.
Tree structures traced in Neurolucida® 360 software can now be visualized in NeuroInfo. After registration and mapping brain images to an atlas, you can view where in the brain the traced neuronal structures are located.
You can now capture Snapshots of the NeuroInfo viewing area while registering images to an atlas. Snapshots are a quick and easy way to generate images for presentation to others.

New AI core enables faster automated cell detections using neural networks.
New descriptions provide more information about the neural networks that can be used .

New Flight Mode enables you to create movies with a first-person perspective, similar to what you would see while wearing a gaming headset.
You can now choose color-channel display on a per-clip basis
There are new export-resolution modes that enable you to create higher resolution movie outputs in some cases.
Progress bars have been added to display the progress of image loading and video export.

You can now map the results of section registration to an intermediate brain space, then use the result to further refine the registration using the Register Volume tools.

This new Map to Intermediate option is available after completing section registration using the Atlas-constrained method; it will create a brain image volume registration that can be further refined for better precision using the
New Nonlinear Registration options provide better results and more control of the parameters used. The new nonlinear registration tools are available for registering either sections or volumes. This method is particularly useful for samples that were distorted during processing or imaging.
Calibration and transform information from image registration are now stored with the data file
Additional tools and information for the Calibration used for registering images to a brain atlas
Improvements to the Tone Mapping filter
New default cursor size to improve visibility
New marker density visualization surface option added to the 3D Visualization window that can help you see your data more clearly.
The updated Gerfen Nissl Mouse Atlas (v2) is more clear and detailed than the previous version. We recommend using to this atlas version.

Serial Section Assembler is the new name for the Brainmaker workflow, that enables you to generate high-resolution 3D volume reconstructions from serial sections imaged with whole slide scanners and research microscopes.
New atlas-constrained section registration provides improved anatomical-region continuity throughout a serial sectioned brain
Better integration with the Allen Mouse Brain Common Coordinate Framework (CCF) for improved registration and visualization of brain regions
Improved nonlinear transformation algorithms for both Register volume and Register sections can improve registration to account for variation between individuals and some deformations introduced by processing.
New image filters in our Batch pipeline combine HDR image sets and filter expansion microscopy specimens to improve analyses

Added the ability to place markers in the 3D visualization window.
The speed of Map Experiment to Atlas for millions of markers has been increased to take minutes instead of hours to complete.

The cell detection workflow interface now has improved progress reporting and filter strength utilization; it also includes new classifiers for cell detection that employ deep learning
Updated image processing and image handling engines that greatly increase capabilities for image analysis and image loading and saving.
Individual color channels can now be displayed using gradient color maps that make it significantly easier to see features in many cases.