DAPI vs Marker-Based Cell Counting: What You Can Measure Without a Nuclear Stain
NeuroQP now supports marker-based cell counting when DAPI is missing. Here is what that workflow can measure, and where DAPI remains better.
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Pascal Dufour, PhD
Pascal got his PhD from the University of Bern in Switzerland in 2015 for biomedical engineering, specifically medical image processing.
He worked for various companies doing AI and deep learning, image processing and scientific software development.
He co-founded NeuroQP in 2026.
Posts by Pascal Dufour, PhD
10x vs 20x Brain Slice Imaging: Accuracy vs Coverage
20x gives higher accuracy, but 10x can cover far more brain area in the same acquisition time. The right choice changes the scope of the study.
Read postCell Detection For DAPI Brain Slices: Options, Tradeoffs, And What Worked For Us
A practical, opinionated look at cell detection for DAPI brain slices, from FIJI workflows to modern algorithms and what actually worked for NeuroQP.
Read postWhy We Are Building NeuroQP: The Missing End-to-End Workflow for Brain Slice Analysis
Neuroscience has strong algorithms, but most labs still lack usable brain slice analysis software that connects registration, cell detection, classification, and statistics.
Read postAutomated DAPI Cell Counting for Mouse Brain Slices: The Count Is Not The Hard Part
Automated DAPI cell counting is not just about detecting nuclei. The real value is a workflow that survives real mouse brain slice datasets.
Read postMouse Brain Atlas Registration: Solve Anatomy on the Whole Slice
Mouse brain atlas registration works best on the 4x whole-slice image, where anatomical context is visible, then carries into 10x or 20x quantification.
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