ArticleProceedings of the National Academy of Sciences of the United States of America2023
3D surface reconstruction of cellular cryo-soft X-ray microscopy tomograms using semisupervised deep learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 19 citations in OpenAlex.
- Emerging approaches for characterizing spatial and temporal dynamics of pathogen-induced organelle remodeling.Cell systems · 2026Review
- Demonstrating soft X-ray tomography in the lab for correlative cryogenic biological imaging using X-rays and light microscopy.Scientific reports · 2025Article
- Automated segmentation of soft X-ray tomography: Native cellular structure with submicron resolution at high-throughput for whole-cell quantitative imaging in yeast.Molecular biology of the cell · 2025Article
- Soft X-ray tomography analysis of mitochondria dynamics in Saccharomyces cerevisiae.Biology direct · 2024Article
- Article
- Automated quantification of vacuole fusion and lipophagy inAutophagy · 2024Article
- Protein nanobarcodes enable single-step multiplexed fluorescence imaging.PLoS biology · 2023Article
- 3D surface reconstruction of cellular cryo-soft X-ray microscopy tomograms using semisupervised deep learning.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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 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.
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