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.
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.
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.
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.
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.
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.
