July 15, 2024DevelopmentOpen Access

A deep learning-based toolkit for 3D nuclei segmentation and quantitative analysis in cellular and tissue context

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Computational study demonstrates accurate 3D nuclei segmentation across plant and animal tissues, highlighting tools for cellular context analysis.

A deep learning pipeline accurately segments 10,000 nuclei across diverse plant and animal tissues, facilitating 3d nuclei segmentation in complex digital organs.
Assessment using cellpose, plantseg, and stardist models enables robust ground truth generation, processing diverse nuclear stains and fluorescent protein reporters.
Highlights cellular organization by using morphographx to link segmented nuclei with surrounding cells and tracking the nuclear-to-cell volume ratio in developing tissues.

Vijayan et al. (2024) studied this question.

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July 15, 2024DevelopmentOpen Access

View Full Paper

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Authors

Computational study demonstrates accurate 3D nuclei segmentation across plant and animal tissues, highlighting tools for cellular context analysis.

A deep learning pipeline accurately segments 10,000 nuclei across diverse plant and animal tissues, facilitating 3d nuclei segmentation in complex digital organs.
Assessment using cellpose, plantseg, and stardist models enables robust ground truth generation, processing diverse nuclear stains and fluorescent protein reporters.
Highlights cellular organization by using morphographx to link segmented nuclei with surrounding cells and tracking the nuclear-to-cell volume ratio in developing tissues.

Vijayan et al. (2024) studied this question.

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