ArticleBioinformatics (Oxford, England)2019
Fully unsupervised symmetry-based mitosis detection in time-lapse cell microscopy.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Deep learning for rapid analysis of cell divisions in vivo during epithelial morphogenesis and repair.eLife · 2024Article
- Article
- Fully unsupervised symmetry-based mitosis detection in time-lapse cell microscopy.Bioinformatics (Oxford, England) · 2019Article
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5 authors.
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Abstract
motivationCell microscopy datasets have great diversity due to variability in cell types, imaging techniques and protocols. Existing methods are either tailored to specific datasets or are based on supervised learning, which requires comprehensive manual annotations. Using the latter approach, however, poses a significant difficulty due to the imbalance between the number of mitotic cells with respect to the entire cell population in a time-lapse microscopy sequence.
resultsWe present a fully unsupervised framework for both mitosis detection and mother-daughters association in fluorescence microscopy data. The proposed method accommodates the difficulty of the different cell appearances and dynamics. Addressing symmetric cell divisions, a key concept is utilizing daughters' similarity. Association is accomplished by defining cell neighborhood via a stochastic version of the Delaunay triangulation and optimization by dynamic programing. Our framework presents promising detection results for a variety of fluorescence microscopy datasets of different sources, including 2D and 3D sequences from the Cell Tracking Challenge. AVAILABILITY AND IMPLEMENTATION: Code is available in github (github.com/topazgl/mitodix). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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