ArticleNature methods2025
Universal consensus 3D segmentation of cells from 2D segmented stacks.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Identifying phenotype-genotype-function coupling in 3D organoid imaging using Shape, Appearance and Motion Phenotype Observation Tool (SPOT).Nature communications · 2026Article
- Deciphering the Multiscale Morphology of Somatic Oncogenic Alterations in Hepatocellular Carcinoma.bioRxiv : the preprint server for biology · 2026Article
- A Machine Vision-Guided Microphysiological Platform With Automated Microfluidics Enables Longitudinal Biomarker Monitoring and Emulation of Translationally Relevant Exposure Scenarios.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Modulation of Oncogenic KRAS Signaling by Branched Actin-driven Cell Membrane Protrusions.Research square · 2026Article
- Volumetric Cyclic Immunofluorescence for 3D Spatial Profiling of Immune Structures in Human FFPE Tissue.bioRxiv : the preprint server for biology · 2026Article
- NucVerse3D: generalizable 3D nuclear instance segmentation across heterogeneous microscopy modalities.Scientific reports · 2026Article
- Article
- Modulation of Oncogenic KRAS Signaling by Branched Actin-driven Cell Membrane Protrusions.bioRxiv : the preprint server for biology · 2026Article
- SpatialDINO: A Self-Supervised 3D Vision Transformer that enables Segmentation and Tracking in Crowded Cellular Environments.bioRxiv : the preprint server for biology · 2026Article
- Unraveling Subcellular Ultrastructure with Cyclically Multiplexed Expansion Microscopy.bioRxiv : the preprint server for biology · 2026Article
- CellSAM: a foundation model for cell segmentation.Nature methods · 2025Article
- Following a cell's every move.Nature methods · 2025Article
- Highly multiplexed 3D profiling of cell states and immune niches in human tumors.Nature methods · 2025Article
- Evaluating the electrical stimulation of bone cells based on an induced transmembrane potential model and intracellular calcium levels.Journal of tissue engineeringArticle
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19 authors.
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Abstract
Cell segmentation is the foundation of a wide range of microscopy-based biological studies. Deep learning has revolutionized two-dimensional (2D) cell segmentation, enabling generalized solutions across cell types and imaging modalities. This has been driven by the ease of scaling up image acquisition, annotation and computation. However, three-dimensional (3D) cell segmentation, requiring dense annotation of 2D slices, still poses substantial challenges. Manual labeling of 3D cells to train broadly applicable segmentation models is prohibitive. Even in high-contrast images annotation is ambiguous and time-consuming. Here we develop a theory and toolbox, u-Segment3D, for 2D-to-3D segmentation, compatible with any 2D method generating pixel-based instance cell masks. u-Segment3D translates and enhances 2D instance segmentations to a 3D consensus instance segmentation without training data, as demonstrated on 11 real-life datasets, comprising >70,000 cells, spanning single cells, cell aggregates and tissue. Moreover, u-Segment3D is competitive with native 3D segmentation, even exceeding when cells are crowded and have complex morphologies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.