Atlas-based imaging data analysis tool for quantitative mouse brain histology (AIDAhisto)

Area of Science:

• Neuroscience
• Computational Biology
• Histology

Background:

• Manual cell counting in neuroscience is crucial but prone to errors and bias.
• Current manual methods are limited to small brain regions.
• Automated tools are needed for large-scale, quantitative cell analysis in whole mouse brains.

Purpose of the Study:

• To develop an algorithm for automated cell counting in mouse brain slices.
• The algorithm should require minimal user input and no pre-training.
• To enable accurate cell quantification within an atlas framework, such as the Allen Mouse Brain atlas.

Main Methods:

• Adapted filter banks to extract features from round-shaped cell nuclei and cellular structures.
• Developed an algorithm named AIDAhisto for automated cell counting.
• Validated results against expert raters and other existing tools.

Main Results:

• AIDAhisto provides accurate and fast cell counting results.
• The algorithm successfully quantifies cell nuclei and various immunohistochemical stainings.
• Demonstrated reliable performance in qualitative and quantitative comparisons.

Conclusions:

• AIDAhisto offers a reliable, automated solution for cell counting in neuroscience.
• The tool enhances the efficiency and accuracy of quantitative cellular analysis in mouse brains.
• Facilitates translational research by enabling whole-brain cell counts within an atlas framework.