Publications [#367856] of Daniel P. Kiehart

Papers Published

1. Haertter, D; Wang, X; Fogerson, SM; Ramkumar, N; Crawford, JM; Poss, KD; Di Talia, S; Kiehart, DP; Schmidt, CF, DeepProjection: specific and robust projection of curved 2D tissue sheets from 3D microscopy using deep learning., Development, vol. 149 no. 21 (November, 2022) [doi]
   (last updated on 2026/01/16)

   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.