Stereology with OPEN-Stereo: low-cost, accessible, and accurate cellular quantification

Area of Science:

• Neuroscience
• Biomedical Engineering
• Computational Biology

Background:

• Accurate assessment of degenerative phenotypes is crucial for neurodegeneration research rigor.
• Stereology is the gold standard for phenotype assessment but is limited by high costs of proprietary systems.
• A need exists for accessible, cost-effective stereology solutions in scientific research.

Purpose of the Study:

• To develop an open-source stereology platform, OPEN-Stereo, to overcome cost barriers.
• To integrate standard microscopy hardware with intelligent software for calibration and positional control.
• To validate the accuracy and reliability of OPEN-Stereo compared to commercial systems.

Main Methods:

• Developed OPEN-Stereo, an open-source platform using computer vision and multi-scale image-based calibration.
• Implemented a global positioning system using open-loop stage data and image analysis, avoiding costly hardware.
• Utilized stereological random sampling for unbiased cell counting and validated with prior commercial system data.

Main Results:

• OPEN-Stereo demonstrated up to 95% agreement in cell counts with commercial systems across brain regions.
• Statistical equivalence was confirmed via two-way repeated measures ANOVA (p = 0.8962).
• The platform maintains accuracy within stereological sampling tolerances without expensive hardware.

Conclusions:

• OPEN-Stereo provides an accessible, cost-effective, and accurate alternative to proprietary stereology systems.
• The platform facilitates rigorous neurodegeneration research by lowering cost barriers.
• OPEN-Stereo's architecture supports future evolution towards AI-driven cell analysis in stereology.