HistoSweep enables cellular-resolution tissue quality control for gigapixel images in digital pathology and spatial omics
Year: 2026
Type: preprint
Abstract: High-resolution histology images are indispensable for pathology and increasingly serve as the structural backbone for spatial omics. Yet whole-slide images (WSIs) frequently contain artifacts, acellular voids, and background regions that, when included in computational workflows, introduce noise, degrade model accuracy, and compromise biological interpretation. Existing tools provide only coarse foreground-background separation, leaving a gap in fine-grained quality control (QC). Here we present HistoSweep, a scalable framework that generates morphology-aware tissue masks at cellular resolution. By integrating density filtering, texture descriptors, and adaptive thresholding, HistoSweep systematically removes non-informative tissue regions while preserving biologically meaningful microstructures. It processes billion-pixel WSIs in minutes on standard CPUs, requiring no GPU acceleration, and is deployable across research and clinical settings. Across 25 WSIs spanning distinct tissues, disease states, and spatial omics platforms, HistoSweep consistently outperformed existing methods. It enhanced visualization and segmentation, improved virtual cell type predictions, and safeguarded spatial transcriptomics integrity by detecting transcript leakage and transcript-histology misalignment. By enabling fine-grained, scalable QC, HistoSweep provides a foundational preprocessing step for reliable and reproducible digital pathology and spatial omics analyses.
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G183623988, G6814219322, Sox9 signaling in lung adenocarcinoma, Using artificial intelligence to discover spatial, genomic, and pathologic biomarkers to guide and augment immune checkpoint inhibitor therapy for gastric cancer, Integration of spatial transcriptomics, genetics, and histomorphology for causal inference in atherosclerotic cardiovascular disease, Integrative analysis of spatial transcriptomics with histology images and single cells, Center for Gastric Pre-Cancer Atlas of Multidimensional Evolution in 3D (GAME3D), Developing informatics tools to predict virtual spatial transcriptomics data with single-cell resolution in large-scale studies
OpenAlex ID: W7127084702