ArticleGenome biology2023
GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Halo: a pretrained model for whole-cell segmentation from nuclei images in spatial transcriptomics.Briefings in bioinformatics · 2026Article
- SegJointGene: joint cell segmentation and spatial gene prioritization by information entropy guided convolutional neural networks.Bioinformatics (Oxford, England) · 2026Article
- Recent advances in methods to characterize archaic introgression in modern humans.Genome research · 2026Review
- RNA2seg: a generalist model for cell segmentation in image-based spatial transcriptomics.Genome biology · 2026Article
- Systematic evaluation of computational methods for cell segmentation.Briefings in bioinformatics · 2026Article
- SpatialRNA: a Python package for easy application of Graph Neural Network models on single-molecule spatial transcriptomics dataset.Bioinformatics (Oxford, England) · 2026Article
- Spatial transcriptomics in Alzheimer's disease: technologies, challenges and discoveries.Molecular neurodegeneration advances · 2026Review
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- AI/ML-Assisted Detection ofInternational journal of molecular sciences · 2025Article
- Cancer therapy resistance from a spatial-omics perspective.Clinical and translational medicine · 2025Review
- Vispro improves imaging analysis for Visium spatial transcriptomics.Genome biology · 2025Article
- Cell simulation as cell segmentation.Nature methods · 2025Article
- Fast analysis of Spatial Transcriptomics (FaST): an ultra lightweight and fast pipeline for the analysis of high resolution spatial transcriptomics.NAR genomics and bioinformatics · 2025Article
- Article
- Article
- State-of-the-Art Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues.Journal of imaging · 2024Review
- A generative benchmark for evaluating the performance of fluorescent cell image segmentation.Synthetic and systems biotechnology · 2024Article
- Bipolar disorder: Construction and analysis of a joint diagnostic model using random forest and feedforward neural networks.IBRO neuroscience reports · 2024Article
- Vispro improves imaging analysis for Visium spatial transcriptomics.bioRxiv : the preprint server for biology · 2024Article
- A systematic evaluation of computational methods for cell segmentation.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
When analyzing data from in situ RNA detection technologies, cell segmentation is an essential step in identifying cell boundaries, assigning RNA reads to cells, and studying the gene expression and morphological features of cells. We developed a deep-learning-based method, GeneSegNet, that integrates both gene expression and imaging information to perform cell segmentation. GeneSegNet also employs a recursive training strategy to deal with noisy training labels. We show that GeneSegNet significantly improves cell segmentation performances over existing methods that either ignore gene expression information or underutilize imaging information.
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