ArticleiScience2024
Automated 3D cytoplasm segmentation in soft X-ray tomography.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Classifying biophysical subpopulations of insulin secretory granules using quantitative whole-cell structure analysis.Structure (London, England : 1993) · 2026Article
- HP1β and H3K9me3 Regulate Olfactory Receptor Choice and Transcriptional Identity.International journal of molecular sciences · 2026Article
- A scoping study of the whole-cell imaging literature as a foundation for the emerging field of cell anatomy.BMC biology · 2026Article
- Demonstrating soft X-ray tomography in the lab for correlative cryogenic biological imaging using X-rays and light microscopy.Scientific reports · 2025Article
- Secretory stimuli distinctly regulate insulin secretory granule maturation through structural remodeling.Structure (London, England : 1993) · 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
- Classifying Biophysical Subpopulations of Insulin Secretory Granules using Quantitative Whole Cell Structure Analysis.bioRxiv : the preprint server for biology · 2025Article
- HP1β and H3K9me3 Regulate Olfactory Receptor Choice and Transcriptional Identity.bioRxiv : the preprint server for biology · 2025Article
- A scoping study of the whole-cell imaging literature: a foundational corpus, potential for mesoscale data synthesis, and implications for standardization of an emerging field.bioRxiv : the preprint server for biology · 2025Article
- Soft X-ray tomography analysis of mitochondria dynamics in Saccharomyces cerevisiae.Biology direct · 2024Article
- How many specimens make a sufficient training set for automated three-dimensional feature extraction?Royal Society open science · 2024Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Cells' structure is key to understanding cellular function, diagnostics, and therapy development. Soft X-ray tomography (SXT) is a unique tool to image cellular structure without fixation or labeling at high spatial resolution and throughput. Fast acquisition times increase demand for accelerated image analysis, like segmentation. Currently, segmenting cellular structures is done manually and is a major bottleneck in the SXT data analysis. This paper introduces ACSeg, an automated 3D cytoplasm segmentation model. ACSeg is generated using semi-automated labels and 3D U-Net and is trained on 43 SXT tomograms of immune T cells, rapidly converging to high-accuracy segmentation, therefore reducing time and labor. Furthermore, adding only 6 SXT tomograms of other cell types diversifies the model, showing potential for optimal experimental design. ACSeg successfully segmented unseen tomograms and is published on Biomedisa, enabling high-throughput analysis of cell volume and structure of cytoplasm in diverse cell types.
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