ArticleScientific reports2019
Automated cell boundary and 3D nuclear segmentation of cells in suspension.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
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- TrueSpot: A robust automated tool for quantifying signal puncta in fluorescent imaging.bioRxiv : the preprint server for biology · 2025Article
- Transcriptional stochasticity reveals multiple mechanisms of long non-coding RNA regulation at the Xist-Tsix locus.Nature communications · 2025Article
- Brain-wide 3D neuron detection and mapping with deep learning.Neurophotonics · 2025Article
- Article
- Comparison between a deep-learning and a pixel-based approach for the automated quantification of HIV target cells in foreskin tissue.Scientific reports · 2024Article
- Phenotypic consequences of logarithmic signaling in MAPK stress response.bioRxiv : the preprint server for biology · 2023Article
- YOLOv5-FPN: A Robust Framework for Multi-Sized Cell Counting in Fluorescence Images.Diagnostics (Basel, Switzerland) · 2023Article
- Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise.Frontiers in cell and developmental biology · 2023Article
- A comprehensive review of computational and image analysis techniques for quantitative evaluation of striated muscle tissue architecture.Biophysics reviews · 2022Review
- Image analysis workflows to reveal the spatial organization of cell nuclei and chromosomes.Nucleus (Austin, Tex.) · 2022Article
- Automated Cell Foreground-Background Segmentation with Phase-Contrast Microscopy Images: An Alternative to Machine Learning Segmentation Methods with Small-Scale Data.Bioengineering (Basel, Switzerland) · 2022Article
- A community-based approach to image analysis of cells, tissues and tumors.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2022Review
- Building predictive signaling models by perturbing yeast cells with time-varying stimulations resulting in distinct signaling responses.STAR protocols · 2021Article
- Learning deep features for dead and living breast cancer cell classification without staining.Scientific reports · 2021Article
- A rate threshold mechanism regulates MAPK stress signaling and survival.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Spatial-omics: Novel approaches to probe cell heterogeneity and extracellular matrix biology.Matrix biology : journal of the International Society for Matrix Biology · 2020Review
- Microscopy cell nuclei segmentation with enhanced U-Net.BMC bioinformatics · 2020Article
- Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregates.Computational and structural biotechnology journal · 2020Review
- Multiplex RNA single molecule FISH of inducible mRNAs in single yeast cells.Scientific data · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
To characterize cell types, cellular functions and intracellular processes, an understanding of the differences between individual cells is required. Although microscopy approaches have made tremendous progress in imaging cells in different contexts, the analysis of these imaging data sets is a long-standing, unsolved problem. The few robust cell segmentation approaches that exist often rely on multiple cellular markers and complex time-consuming image analysis. Recently developed deep learning approaches can address some of these challenges, but they require tremendous amounts of data and well-curated reference data sets for algorithm training. We propose an alternative experimental and computational approach, called CellDissect, in which we first optimize specimen preparation and data acquisition prior to image processing to generate high quality images that are easier to analyze computationally. By focusing on fixed suspension and dissociated adherent cells, CellDissect relies only on widefield images to identify cell boundaries and nuclear staining to automatically segment cells in two dimensions and nuclei in three dimensions. This segmentation can be performed on a desktop computer or a computing cluster for higher throughput. We compare and evaluate the accuracy of different nuclear segmentation approaches against manual expert cell segmentation for different cell lines acquired with different imaging modalities.
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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.