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Peer-reviewedCC BYScientific data

A large-scale expert-annotated 3D image dataset of dermal tissue macrophages.

  • Amirhossein Kardoost1,2
  • Nisreen Ghanem3,4
  • Tianru Li5
  • Julia Kolter6
  • Carsten Marr7,8,9,10,11
11 affiliations
  1. 1Institute of AI for Health & Helmholtz AI, Computational Health Center, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany.
  2. 2Munich Center for Machine Learning (MCML), Munich, Germany.
  3. 3Institute for Infection Prevention and Control, Medical Center and Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  4. 4Faculty of Biology, University of Freiburg, Freiburg, Germany.
  5. 5Department of Mathematics, Technical University of Munich, Munich, Germany.
  6. 6Institute for Infection Prevention and Control, Medical Center and Faculty of Medicine, University of Freiburg, Freiburg, Germany. [email protected].
  7. 7Institute of AI for Health & Helmholtz AI, Computational Health Center, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany. [email protected].
  8. 8Munich Center for Machine Learning (MCML), Munich, Germany. [email protected].
  9. 9Department of Medicine III, Ludwig-Maximilian-University Hospital, Munich, Germany. [email protected].
  10. 10Department of Physics, Ludwig-Maximilian-University, Munich, Germany. [email protected].
  11. 11German Cancer Consortium, DKTK, partner site Munich, Germany. [email protected].
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Abstract

Tissue macrophages are highly versatile immune cells whose morphologies shift dramatically depending on their activation state and tissue environment. This morphological plasticity reflects their diverse origins and functions but also poses significant challenges for both manual and automated image analysis. In the skin, macrophages display particularly intricate shapes, ranging from rounded to highly elongated, irregular, and non-convex forms. We developed a deep learning-based approach for the automated identification, 3D segmentation, and quantitative morphological profiling of dermal tissue macrophages in mice, facilitating large-scale analysis of cellular volume and elongation. This allowed us to build a dataset of 3D-segmented dermal tissue macrophages, comprising 947 single cells. To ensure accuracy, automatically generated segmentations were refined using a custom-designed Napari plugin. To illustrate technical validity and potential reuse, we quantified macrophage volume and elongation in adult steady-state and five-day post-Staphylococcus aureus infection samples, showing that the dataset captures biologically interpretable morphological variation.

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Cite

BibTeXRIS
BibTeX
@article{kardoost2026largescale,
  title = {{A large-scale expert-annotated 3D image dataset of dermal tissue macrophages.}},
  author = {Amirhossein Kardoost and Nisreen Ghanem and Tianru Li and Julia Kolter and Carsten Marr},
  journal = {Scientific data},
  year = {2026},
  month = oct,
  doi = {10.1038/s41597-026-08556-7},
  url = {https://europepmc.org/article/MED/42901954},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - A large-scale expert-annotated 3D image dataset of dermal tissue macrophages.
AU  - Amirhossein Kardoost
AU  - Nisreen Ghanem
AU  - Tianru Li
AU  - Julia Kolter
AU  - Carsten Marr
PY  - 2026
DA  - 2026/10/20
JO  - Scientific data
DO  - 10.1038/s41597-026-08556-7
UR  - https://europepmc.org/article/MED/42901954
AB  - Tissue macrophages are highly versatile immune cells whose morphologies shift dramatically depending on their activation state and tissue environment. This morphological plasticity reflects their diverse origins and functions but also poses significant challenges for both manual and automated image analysis. In the skin, macrophages display particularly intricate shapes, ranging from rounded to highly elongated, irregular, and non-convex forms. We developed a deep learning-based approach for the automated identification, 3D segmentation, and quantitative morphological profiling of dermal tissue macrophages in mice, facilitating large-scale analysis of cellular volume and elongation. This allowed us to build a dataset of 3D-segmented dermal tissue macrophages, comprising 947 single cells. To ensure accuracy, automatically generated segmentations were refined using a custom-designed Napari plugin. To illustrate technical validity and potential reuse, we quantified macrophage volume and elongation in adult steady-state and five-day post-Staphylococcus aureus infection samples, showing that the dataset captures biologically interpretable morphological variation.
SN  - 2052-4463
ER  -