ArticleDevelopment (Cambridge, England)2024
A deep learning-based toolkit for 3D nuclei segmentation and quantitative analysis in cellular and tissue context.
Article in Development (Cambridge, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Unlocking the full potential of spatial omics in plants: practical challenges, solutions, and a path forward.The Plant cell · 2026Review
- Time-dependent bistability leads to critical slowing down during floral transition in Arabidopsis.Nature communications · 2026Article
- Altered chromatin accessibility and nucleosome positioning landscape upon HDAC and LSD1 inhibition in cancer cell.bioRxiv : the preprint server for biology · 2026Article
- RetinaDetachNet: Automated Deep Learning Quantification of Photoreceptor Cell Death for Neuroprotection Studies in Experimental Retinal Detachment.Translational vision science & technology · 2026Article
- Ultrack: pushing the limits of cell tracking across biological scales.Nature methods · 2025Article
- In toto live imaging of Erk signaling dynamics in developing zebrafish hepatocytes.Developmental biology · 2025Article
- Nellie: automated organelle segmentation, tracking and hierarchical feature extraction in 2D/3D live-cell microscopy.Nature methods · 2025Article
- A Method to Visualize Cell Proliferation ofPlant direct · 2025Article
- The pipelines of deep learning-based plant image processing.Quantitative plant biology · 2025Review
- Artificial Intelligence for Organelle Segmentation in Live-Cell Imaging.Research (Washington, D.C.) · 2025Review
- State-of-the-Art Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues.Journal of imaging · 2024Review
- Nuclear instance segmentation and tracking for preimplantation mouse embryos.Development (Cambridge, England) · 2024Article
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Authors and funding
11 authors.
Funding
Abstract
We present a new set of computational tools that enable accurate and widely applicable 3D segmentation of nuclei in various 3D digital organs. We have developed an approach for ground truth generation and iterative training of 3D nuclear segmentation models, which we applied to popular CellPose, PlantSeg and StarDist algorithms. We provide two high-quality models trained on plant nuclei that enable 3D segmentation of nuclei in datasets obtained from fixed or live samples, acquired from different plant and animal tissues, and stained with various nuclear stains or fluorescent protein-based nuclear reporters. We also share a diverse high-quality training dataset of about 10,000 nuclei. Furthermore, we advanced the MorphoGraphX analysis and visualization software by, among other things, providing a method for linking 3D segmented nuclei to their surrounding cells in 3D digital organs. We found that the nuclear-to-cell volume ratio varies between different ovule tissues and during the development of a tissue. Finally, we extended the PlantSeg 3D segmentation pipeline with a proofreading tool that uses 3D segmented nuclei as seeds to correct cell segmentation errors in difficult-to-segment tissues.
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Registered trials
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.