ArticleNPJ systems biology and applications2020
3D convolutional neural networks-based segmentation to acquire quantitative criteria of the nucleus during mouse embryogenesis.
Article in NPJ systems biology and applications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- 3DCellComposer - A versatile pipeline utilizing 2D cell segmentation methods for 3D cell segmentation.Methods (San Diego, Calif.) · 2025Article
- Zinc eluted from glassware is a risk factor for embryo development in human and animal assisted reproduction†.Biology of reproduction · 2025Article
- Nuclear instance segmentation and tracking for preimplantation mouse embryos.Development (Cambridge, England) · 2024Article
- Seg2Link: an efficient and versatile solution for semi-automatic cell segmentation in 3D image stacks.Scientific reports · 2023Article
- Contributions of deep learning to automated numerical modelling of the interaction of electric fields and cartilage tissue based on 3D images.Frontiers in bioengineering and biotechnology · 2023Article
- Marker-controlled watershed with deep edge emphasis and optimized H-minima transform for automatic segmentation of densely cultivated 3D cell nuclei.BMC bioinformatics · 2022Article
- Efficient automatic 3D segmentation of cell nuclei for high-content screening.BMC bioinformatics · 2022Article
- Deep learning -- promises for 3D nuclear imaging: a guide for biologists.Journal of cell science · 2022Review
- Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets.PLoS computational biology · 2022Review
- A reusable neural network pipeline for unidirectional fiber segmentation.Scientific data · 2022Article
- Number of necessary training examples for Neural Networks with different number of trainable parameters.Journal of pathology informatics · 2022Article
- Imaging developmental cell cycles.Biophysical journal · 2021Review
- Deep learning for bioimage analysis in developmental biology.Development (Cambridge, England) · 2021Review
- Size scaling in collective cell growth.Development (Cambridge, England) · 2021Article
- Segmentor: a tool for manual refinement of 3D microscopy annotations.BMC bioinformatics · 2021Article
- Agarose microgel culture delineates lumenogenesis in naive and primed human pluripotent stem cells.Stem cell reports · 2021Article
- Nuclei Detection for 3D Microscopy With a Fully Convolutional Regression Network.IEEE access : practical innovations, open solutions · 2021Article
- Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregates.Computational and structural biotechnology journal · 2020Review
- Reorganization, specialization, and degradation of oocyte maternal components for early development.Reproductive medicine and biologyReview
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8 authors.
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Abstract
During embryogenesis, cells repeatedly divide and dynamically change their positions in three-dimensional (3D) space. A robust and accurate algorithm to acquire the 3D positions of the cells would help to reveal the mechanisms of embryogenesis. To acquire quantitative criteria of embryogenesis from time-series 3D microscopic images, image processing algorithms such as segmentation have been applied. Because the cells in embryos are considerably crowded, an algorithm to segment individual cells in detail and accurately is needed. To quantify the nuclear region of every cell from a time-series 3D fluorescence microscopic image of living cells, we developed QCANet, a convolutional neural network-based segmentation algorithm for 3D fluorescence bioimages. We demonstrated that QCANet outperformed 3D Mask R-CNN, which is currently considered as the best algorithm of instance segmentation. We showed that QCANet can be applied not only to developing mouse embryos but also to developing embryos of two other model species. Using QCANet, we were able to extract several quantitative criteria of embryogenesis from 11 early mouse embryos. We showed that the extracted criteria could be used to evaluate the differences between individual embryos. This study contributes to the development of fundamental approaches for assessing embryogenesis on the basis of extracted quantitative criteria.
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