Journal Article

3D surface reconstruction of cellular cryo-soft X-ray microscopy tomograms using semisupervised deep learning

Moynova, Ralitsa, Dyhr, Michael C. A., Sadeghi, Mohsen, Ewers, Helge, Çakmak, Burcu Kepsutlu, Werner, Stephan, Noé, Frank, Knappe, Carolin, Schneider, Gerd, McNally, James

2023

Overview

Cryo-soft X-ray tomography (cryo-SXT) is a powerful method to investigate the ultrastructure of cells, offering resolution in the tens of nanometer range and strong contrast for membranous structures without requiring labeling or chemical fixation. The short acquisition time and the relatively large field of view leads to fast acquisition of large amounts of tomographic image data. Segmentation of these data into accessible features is a necessary step in gaining biologically relevant information from cryo-soft X-ray tomograms. However, manual image segmentation still requires several orders of magnitude more time than data acquisition. To address this challenge, we have here developed an end-to-end automated 3D segmentation pipeline based on semisupervised deep learning. Our approach is suitable for high-throughput analysis of large amounts of tomographic data, while being robust when faced with limited manual annotations and variations in the tomographic conditions. We validate our approach by extracting three-dimensional information on cellular ultrastructure and by quantifying nanoscopic morphological parameters of filopodia in mammalian cells.

• Publisher: National Academy of Sciences
• Subject: Animals / Biological Sciences / Cell Biology / Cryoelectron Microscopy / Data acquisition / Deep Learning / Filopodia / Image acquisition / Image processing / Image Processing, Computer-Assisted - methods / Image reconstruction / Image segmentation / Mammalian cells / Mammals / Microscopy, Fluorescence - methods / Semi-supervised learning / Soft x rays / Tomography, X-Ray - methods / Ultrastructure / X ray microscopy / X-Rays