Automated brain slice analysis for neuroscience workflows

Quantify cells across DAPI, NeuN, and marker-based projects with atlas registration, cell detection and classification, and region-level statistics for 4x reference workflows or full-brain 10x and 20x slices.

Register the Slice Once, then Analyze any Brain Region

Use NeuroQP with overview-plus-detail imaging or full-brain high-resolution slices.

NeuroQP maps your slice into atlas space so every detection can be tied back to anatomy. It works whether your study uses a low-magnification reference with detailed fields of view, or full-brain high-resolution images. After registration review, your cell detections and classifications flow into brain-region statistics automatically.

Whole 4x mouse brain slice with atlas overlay
Zoomed atlas registration points on a mouse brain slice
20x staining aligned to registered 4x atlas reference

Detect Cells from the Stains You Already Use

Use DAPI, NeuN, or marker channels as the source for cell detection.

NeuroQP adapts to the biology visible in your images. Use DAPI or NeuN when you have a clear nuclei or cell-source stain, or detect marker-positive cells directly from stains such as cFos or TRAP when that is what the experiment provides. The goal is straightforward: turn visible cells into reviewable detections that can be classified and quantified by brain region.

Whole rat brain section with NeuroQP cell detections
Medium zoom of rat brain tissue with NeuroQP cell detections
Detailed rat brain tissue view with NeuroQP cell detections
20x DAPI image with automated cell detections

Source image: Maric et al. (2021), Figshare, CC BY 4.0. Adapted with NeuroQP detection overlays.

Classify Cells with Ready-to-Use or Custom Models

Start with pretrained cFos and TRAP-style classifiers, or train a model for your staining.

NeuroQP includes classifiers for common cFos and TRAP-style IHC, so many projects can begin without building a model from scratch. If your staining looks different, you can label examples from your own images and train a project-specific classifier. The result is a consistent way to separate true signal from background and quantify the cells that matter.

Rat brain tissue with NeuroQP cell classification overlay
20x staining image with classified cells
Classifier training curve showing F1 score and loss over 123 epochs

Source image: Maric et al. (2021), Figshare, CC BY 4.0. Adapted with NeuroQP classification overlays.

Export Publication-Ready Brain Region Plots

Build regional plots from quantified IHC data, then use them directly or edit the SVG output.

NeuroQP brings atlas registration, cell detection, and classification results together in one place. Compare counts, densities, and marker-positive cells across animals, groups, and brain regions. The plots are designed to be used directly in publications, or exported as SVG files for final editing in Illustrator, Inkscape, or your figure workflow.

Anatomical brain region plot comparing group X and group Z
Cell density heatmaps across four experimental groups
Region-free cell map overlaid on a mouse brain atlas
Box and violin plot comparing neuroscience conditions across brain regions
Region-free cell density heatmap overlaid on a mouse brain atlas
Mirrored brain region plot comparing cell percentages and density

Frequently asked questions

NeuroQP is an AI-powered image analysis platform built for neuroscience researchers. It automates cell detection and classification, brain atlas registration, and region-level statistics from mouse brain slice images, including DAPI, NeuN, and marker-based projects.

Move brain slice quantification out of spreadsheets

Join the waitlist to access automated brain slice quantification for DAPI, NeuN, and marker-based projects. Upload your images, review the workflow, and export region-level results without plugins, scripts, or manual counting.