Researchers have introduced HistoSweep, a computationally efficient and scalable framework designed for high-resolution tissue quality control in digital pathology and spatial omics. The tool addresses a critical gap in the field by moving beyond simple foreground-background separation to generate cellular-resolution masks that exclude artifacts, acellular voids, and noise. By integrating color statistics, texture descriptors, and adaptive thresholding, HistoSweep preserves biologically meaningful microstructures without requiring expensive GPU resources. Experiments across 25 diverse datasets demonstrate that the system consistently outperforms existing methods, enhancing visualization and improving the accuracy of cell-type predictions. Furthermore, it serves as a vital safeguard for spatial transcriptomics, identifying data integrity issues like transcript leakage and image misalignment. Ultimately, HistoSweep provides a foundational preprocessing step that ensures more reliable and reproducible biological interpretations of billion-pixel images.

References:

• Schroeder A, Yu X, Li W, et al. HistoSweep enables cellular-resolution tissue quality control for gigapixel images in digital pathology and spatial omics[J]. bioRxiv, 2026: 2026.01. 30.702675.