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

ABSTRACT: 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 s

SUBMITTER: Dyhr MCA 

PROVIDER: S-EPMC10268598 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature