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Winnok De Vos

As seen in: BioRxiv
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Articles

Unbiased identification of cell identity in dense mixed neural cultures

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By Sarah De Beuckeleer, Peter Ponsaerts, Winnok De Vos
| BioRxiv
Abstract Induced pluripotent stem cell (iPSC) technology is revolutionizing cell biology. However, the variability between individual iPSC lines and the lack of efficient technology to comprehensively characterize iPSC-derived cell types hinder its adoption in routine preclinical screening settings.
Open in Who Shared Issue with article?

PAC-MAP: Proximity Adjusted Centroid Mapping for Accurate Detection of Nuclei in Dense 3D Cell Systems

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By Sarah De Beuckeleer, Frederik De Smet, Jan Sijbers, Winnok De Vos
| BioRxiv
Abstract In the past decade, deep learning algorithms have surpassed the performance of many conventional image segmentation pipelines. Powerful models are now available for segmenting cells and nuclei in diverse 2D image types, but segmentation in 3D cell systems remains challenging due to the high cell density, the heterogenous resolution and contrast across the image volume, and the difficulty in generating reliable and sufficient ground truth data for model training.
Open in Who Shared Issue with article?

Unbiased identification of cell identity in dense mixed neural cultures

|
By Sarah De Beuckeleer, Peter Ponsaerts, Winnok De Vos
| BioRxiv
Abstract Induced pluripotent stem cell (iPSC) technology is revolutionizing cell biology. However, the variability between individual iPSC lines and the lack of efficient technology to comprehensively characterize iPSC-derived cell types hinder its adoption in routine screening settings.
Open in Who Shared Issue with article?
See all 3 articles