ArticleDevelopment (Cambridge, England)2022
DeepProjection: specific and robust projection of curved 2D tissue sheets from 3D microscopy using deep learning.
Article in Development (Cambridge, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 23 citations in OpenAlex.
- A toolkit for mapping cell identities in relation to neighbors reveals conserved patterning of neuromesodermal progenitor populations.PLoS biology · 2025Article
- A minimal vertex model explains how the amnioserosa avoids fluidization duringProceedings of the National Academy of Sciences of the United States of America · 2025Article
- Bellybutton: accessible and customizable deep-learning image segmentation.Scientific reports · 2024Article
- Article
- Mechano-chemical regulation of complex cell shape formation: Epidermal pavement cells-A case study.Quantitative plant biology · 2023Review
- DeepProjection: specific and robust projection of curved 2D tissue sheets from 3D microscopy using deep learning.Development (Cambridge, England) · 2022Article
- Extracting multiple surfaces from 3D microscopy images in complex biological tissues with the Zellige software tool.BMC biology · 2022Article
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Authors and funding
9 authors at 1 institution in 2 countries.
Funding
Abstract
The efficient extraction of image data from curved tissue sheets embedded in volumetric imaging data remains a serious and unsolved problem in quantitative studies of embryogenesis. Here, we present DeepProjection (DP), a trainable projection algorithm based on deep learning. This algorithm is trained on user-generated training data to locally classify 3D stack content, and to rapidly and robustly predict binary masks containing the target content, e.g. tissue boundaries, while masking highly fluorescent out-of-plane artifacts. A projection of the masked 3D stack then yields background-free 2D images with undistorted fluorescence intensity values. The binary masks can further be applied to other fluorescent channels or to extract local tissue curvature. DP is designed as a first processing step than can be followed, for example, by segmentation to track cell fate. We apply DP to follow the dynamic movements of 2D-tissue sheets during dorsal closure in Drosophila embryos and of the periderm layer in the elongating Danio embryo. DeepProjection is available as a fully documented Python package.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.