DeepProjection: specific and robust projection of curved 2D tissue sheets from 3D microscopy using deep learning.

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

SUBMITTER: Haertter D 

PROVIDER: S-EPMC9686994 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature