Enhancing digital pathology workflows: computational blur detection for H&E image quality control in preclinical toxicology

Toxicologic pathology is undergoing a digital transformation, with advances in imaging and computational methods enabling automation of traditionally manual workflows. Central to these digital workflows is the generation of high-quality whole slide images (WSIs), where one key determinant of image quality is focus sharpness. To address this, we have integrated a pair of productionalized computational models - 'MiQC' (Microscopic Quality Control) - into our routine image QC workflows. MiQC combines Local Binary Patterns (LBP) and DeepFocus-based deep learning algorithms to detect and quantify out-of-focus regions in WSIs. Subsequent to scanner-based focus metric assessment, MiQC further screens WSIs and supports technician review by generating heatmaps that highlight problematic areas. Even WSIs with scanner focus scores of 98-99% can contain unacceptable blur, which MiQC helps identify. Using this system, 85-95% of WSIs are approved without further intervention, and technician review time is reduced by nearly 50%. Compared to fully manual review, MiQC has doubled our throughput of QC'd slides per hour. This efficiency gain has accelerated the expansion of our high-quality WSI repository and provides a scalable, reproducible framework for enhancing image QC in toxicologic and broader digital pathology applications. MiQC supports higher throughput and integration of automated image analysis pipelines, laying the groundwork for robust downstream computational pathology workflows.