Year: 2026
Type: preprint
Abstract: Microglial morphology analysis has emerged as a critical approach in neuroinflammation research. The increasing availability of high-resolution and whole-slide histology images has created strong demand for robust, scalable analytical methods capable of efficiently processing these datasets. Existing approaches may require code-based local computation or prolonged processing times for whole-slide images, while manual morphology quantification remains labor-intensive. To address these limitations, we introduce StainAI-Web, a server that performs rapid, automated microglial morphology detection across whole-slide immunohistochemistry images in the cloud. The system provides interactive grayscale preprocessing and supports ROI-based regional quantification, allowing users to define specific tissue regions and systematically compare microglial populations across regions. An interactive visualization module dynamically updates quantitative summaries of morphological distributions as ROIs are selected or modified. Analysis results, including structured JSON files and morphology-annotated overlays, are downloadable for downstream analysis or integration into external computational pipelines. These integrated features provide an accessible framework for efficient and reproducible whole-slide microglial morphology analysis. The StainAI-Web is available at https://stainaiviewer.azurewebsites.net/ .
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: green
Awards: Excellence in Research: PathoRadi ‒ an interactive web server for AI-assisted radiologic-pathologic image analysis, correlation and visualization, Catalyst Project: Quantification of immunohistochemistry images of neuroglia, G8568809525, Howard University Research Center for Minority Health and Health Disparities
OpenAlex ID: W7222921035