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DeepProjection: specific and robust projection of curved 2D tissue sheets from 3D microscopy using deep learning.
- Source :
-
Development (Cambridge, England) [Development] 2022 Nov 01; Vol. 149 (21). Date of Electronic Publication: 2022 Nov 11. - Publication Year :
- 2022
-
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.<br />Competing Interests: Competing interests The authors declare no competing or financial interests.<br /> (© 2022. Published by The Company of Biologists Ltd.)
Details
- Language :
- English
- ISSN :
- 1477-9129
- Volume :
- 149
- Issue :
- 21
- Database :
- MEDLINE
- Journal :
- Development (Cambridge, England)
- Publication Type :
- Academic Journal
- Accession number :
- 36178108
- Full Text :
- https://doi.org/10.1242/dev.200621