Development of deep learning models for microglia analyses in brain tissue using DeePathology™ STUDIO

Background: Interest in artificial intelligence-driven analysis of medical images has seen a steep increase in recent years. Thus, our paper aims to promote and facilitate the use of this state-of-the-art technology to fellow researchers and clinicians.

New method: We present custom deep learning models generated in DeePathology™ STUDIO without the need for background knowledge in deep learning and computer science underlined by practical suggestions.

Results: We describe the general workflow in this commercially available software and present three real-world examples how to detect microglia on IBA1-stained mouse brain sections including their differences, validation results and analysis of a sample slide.

Comparison with existing methods: Deep-learning assisted analysis of histological images is faster than classical analysis methods, and offers a wide variety of detection possibilities that are not available using methods based on staining intensity.

Conclusions: Reduced researcher bias, increased speed and extended possibilities make deep-learning assisted analysis of histological images superior to traditional analysis methods for histological images.