ArticleDevelopment (Cambridge, England)2024
Nuclear instance segmentation and tracking for preimplantation mouse embryos.
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 10 papers.
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Who cites it
10 citing papers in PubMed.
- Synthetic data enables human-grade microtubule analysis with foundation models for segmentation.PLoS computational biology · 2026Article
- Cell tracking with accurate error prediction.Nature methods · 2025Article
- A model for boundary-driven tissue morphogenesis.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Live imaging endogenous transcription factor dynamics reveals mechanisms of epiblast and primitive endoderm fate segregation.Current biology : CB · 2025Article
- Generative model for the first cell fate bifurcation in mammalian development.Development (Cambridge, England) · 2025Article
- Geometric, cell cycle and maternal-to-zygotic transition-associated YAP dynamics during preimplantation embryo development.Developmental biology · 2025Article
- Article
- Geometric, cell cycle and maternal-to-zygotic transition-associated YAP dynamics during preimplantation embryo development.bioRxiv : the preprint server for biology · 2025Article
- A tug-of-war between germ cell motility and intercellular bridges controls germline cyst formation in mice.Current biology : CB · 2024Article
- A deep learning-based toolkit for 3D nuclei segmentation and quantitative analysis in cellular and tissue context.Development (Cambridge, England) · 2024Article
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Authors and funding
17 authors.
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Abstract
For investigations into fate specification and morphogenesis in time-lapse images of preimplantation embryos, automated 3D instance segmentation and tracking of nuclei are invaluable. Low signal-to-noise ratio, high voxel anisotropy, high nuclear density, and variable nuclear shapes can limit the performance of segmentation methods, while tracking is complicated by cell divisions, low frame rates, and sample movements. Supervised machine learning approaches can radically improve segmentation accuracy and enable easier tracking, but they often require large amounts of annotated 3D data. Here, we first report a previously unreported mouse line expressing near-infrared nuclear reporter H2B-miRFP720. We then generate a dataset (termed BlastoSPIM) of 3D images of H2B-miRFP720-expressing embryos with ground truth for nuclear instances. Using BlastoSPIM, we benchmark seven convolutional neural networks and identify Stardist-3D as the most accurate instance segmentation method. With our BlastoSPIM-trained Stardist-3D models, we construct a complete pipeline for nuclear instance segmentation and lineage tracking from the eight-cell stage to the end of preimplantation development (>100 nuclei). Finally, we demonstrate the usefulness of BlastoSPIM as pre-train data for related problems, both for a different imaging modality and for different model systems.
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