Automated 3D cytoplasm segmentation in soft X-ray tomography

Ayse Erozan; Philipp D. Lösel; Vincent Heuveline; Venera Weinhardt

iScience (Jun 2024)

Automated 3D cytoplasm segmentation in soft X-ray tomography

Ayse Erozan,
Philipp D. Lösel,
Vincent Heuveline,
Venera Weinhardt

Affiliations

Ayse Erozan: Centre for Organismal Studies, Heidelberg University, Heidelberg, Germany; Engineering Mathematics and Computing Lab, Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany; Data Mining and Uncertainty Quantification, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany; Corresponding author
Philipp D. Lösel: Engineering Mathematics and Computing Lab, Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany; Data Mining and Uncertainty Quantification, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany; Department of Materials Physics Research School of Physics, The Australian National University, Acton ACT, Australia
Vincent Heuveline: Engineering Mathematics and Computing Lab, Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany; Data Mining and Uncertainty Quantification, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
Venera Weinhardt: Centre for Organismal Studies, Heidelberg University, Heidelberg, Germany; Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; Corresponding author

Journal volume & issue: Vol. 27, no. 6
p. 109856

Abstract

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Summary: 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.

Published in iScience

ISSN: 2589-0042 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Science
Website: http://www.cell.com/iscience/home

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