ArticleScientific reports2025
Using transcripts to refine image based cell segmentation with FastReseg.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- STAT3D as a new image-tailored Xenium-based spatial transcriptomics tool for accurately dissecting the tumor microenvironment.Nucleic acids research · 2026Article
- Cardiac tertiary immune niches drive immune activation in immune checkpoint inhibitor myocarditis.Science advances · 2026Article
- Eosinophil diversity and function: emerging insights into subtypes and therapeutic targets.Cell death discovery · 2026Review
- Imaging-Based Spatial Transcriptomics: Data Interpretation Methods and Biomedical Applications.Biology · 2026Review
- Resolving sensitivity, specificity and signal contamination in Xenium spatial transcriptomics.Nature methods · 2026Article
- Reconstructing biologically coherent cellular profiles from imaging-based spatial transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Spatial transcriptomics in Alzheimer's disease: technologies, challenges and discoveries.Molecular neurodegeneration advances · 2026Review
- MisTIC: Missegmented Transcript Inference Correction for Improved Spatial Transcriptomics Analysis.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatial transcriptomics (ST) faces persistent challenges in cell segmentation accuracy, which can bias biological interpretations in a spatial-dependent way. FastReseg introduces a novel algorithm that refines inaccuracies in existing image-based segmentations using transcriptomic data, without radically redefining cell boundaries. By combining image-based information with 3D transcriptomic precision, FastReseg enhances segmentation accuracy. Its key innovation, a transcript scoring system based on log-likelihood ratios, facilitates the quick identification and correction of spatial doublets caused by cell proximity or overlap in 2D. FastReseg reduces circularity in boundary derivation, and addresses computational challenges with a modular workflow designed for large datasets. The algorithm's modularity allows for seamless optimization and integration of advancements in segmentation technology. FastReseg provides a scalable, efficient solution to improve the quality and interpretability of ST data, ensuring compatibility with evolving segmentation methods and enabling more accurate biological insights.
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Registered trials
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