ArticleBriefings in bioinformatics2024
A systematic evaluation of computational methods for cell segmentation.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Halo: a pretrained model for whole-cell segmentation from nuclei images in spatial transcriptomics.Briefings in bioinformatics · 2026Article
- Transforming subcellular spatial transcriptomics: deep learning models for cell segmentation.Nucleic acids research · 2026Review
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Persistent homology-based segmentation tool for membrane images.Cell reports methods · 2026Article
- CSRefiner: a lightweight framework for fine-tuning cell segmentation models with small datasets.Briefings in bioinformatics · 2026Article
- Spatial transcriptomics in Alzheimer's disease: technologies, challenges and discoveries.Molecular neurodegeneration advances · 2026Review
- Spatial omics for profiling the dynamic tumor microenvironment.Clinical & translational immunology · 2026Review
- MisTIC: Missegmented Transcript Inference Correction for Improved Spatial Transcriptomics Analysis.bioRxiv : the preprint server for biology · 2025Article
- Spatial Transcriptomics of Adipose Tissue: Technologies, Applications, and Challenges.Journal of obesity & metabolic syndrome · 2025Review
- Vispro improves imaging analysis for Visium spatial transcriptomics.Genome biology · 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
- Single-cell spatial transcriptomics unravels cell states and ecosystems associated with clinical response to immunotherapy.Journal for immunotherapy of cancer · 2025Article
- Deep learning-based automated detection of endometrioid endometrial carcinoma in histopathology.Frontiers in oncology · 2025Article
- Artificial Intelligence for Organelle Segmentation in Live-Cell Imaging.Research (Washington, D.C.) · 2025Review
- Deep Learning-Enabled Integration of Histology and Transcriptomics for Tissue Spatial Profile Analysis.Research (Washington, D.C.) · 2025Article
- Vispro improves imaging analysis for Visium spatial transcriptomics.bioRxiv : the preprint server for biology · 2024Article
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9 authors.
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Abstract
Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation and instance segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform cell nuclei and whole cell segmentation using light microscopy and fluorescence staining images. We found that general-purpose methods incorporating the attention mechanism exhibit the best overall performance. We identified various factors influencing segmentation performances, including image channels, choice of training data, and cell morphology, and evaluated the generalizability of methods across image modalities. We also provide guidelines for choosing the optimal segmentation methods in various real application scenarios. We developed Seggal, an online resource for downloading segmentation models already pre-trained with various tissue and cell types, substantially reducing the time and effort for training cell segmentation models.
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