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Peer-reviewedCC BY 4.0Scientific Data

BEAMSTER: Brain mEtAstases segMentation for STEreotactic Radiotherapy, A Retrospective MRI Dataset with Expert Segmentations

  • Michal Nohel1,2
  • Stefan Reguli3
  • Romana Kaplanova1
  • Jana Jackaninova4
  • Jiri Chmelik2
  • Lukas Knybel4
4 affiliations
  1. 1Department of Deputy Director for Science and Research, University Hospital & Faculty of Medicine, Ostrava, 708 52 Czech Republic
  2. 2Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, 616 00 Czech Republic
  3. 3Department of Neurosurgery, University Hospital & Faculty of Medicine, Ostrava, 708 52 Czech Republic
  4. 4Department of Oncology, University Hospital & Faculty of Medicine, Ostrava, 708 52 Czech Republic
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Abstract

We present a retrospective dataset of contrast-enhanced T1-weighted magnetic resonance imaging scans from 140 patients with brain metastases who underwent stereotactic radiotherapy. In total, 260 metastatic lesions were annotated by an experienced radiation oncologist using a radiotherapy planning system, and the resulting binary segmentation masks were converted into NIfTI format. All data were de-identified prior to release, and facial features were removed from imaging data using a defacing procedure to ensure patient privacy. The dataset represents a valuable resource for the development and validation of computer-aided detection and segmentation methods, including those based on machine learning. A particular strength is the inclusion of small metastatic lesions, which are clinically relevant yet challenging to detect and segment automatically. We anticipate that this collection will facilitate reproducible research and support the development of robust algorithms for clinical translation in radiotherapy planning.

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Cite

BibTeXRIS
BibTeX
@article{nohel2026beamster,
  title = {{BEAMSTER: Brain mEtAstases segMentation for STEreotactic Radiotherapy, A Retrospective MRI Dataset with Expert Segmentations}},
  author = {Michal Nohel and Stefan Reguli and Romana Kaplanova and Jana Jackaninova and Jiri Chmelik and Lukas Knybel},
  journal = {Scientific Data},
  year = {2026},
  month = jul,
  doi = {10.1038/s41597-026-07777-0},
  url = {https://doi.org/10.1038/s41597-026-07777-0},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - BEAMSTER: Brain mEtAstases segMentation for STEreotactic Radiotherapy, A Retrospective MRI Dataset with Expert Segmentations
AU  - Michal Nohel
AU  - Stefan Reguli
AU  - Romana Kaplanova
AU  - Jana Jackaninova
AU  - Jiri Chmelik
AU  - Lukas Knybel
PY  - 2026
DA  - 2026/07/01
JO  - Scientific Data
DO  - 10.1038/s41597-026-07777-0
UR  - https://doi.org/10.1038/s41597-026-07777-0
AB  - We present a retrospective dataset of contrast-enhanced T1-weighted magnetic resonance imaging scans from 140 patients with brain metastases who underwent stereotactic radiotherapy. In total, 260 metastatic lesions were annotated by an experienced radiation oncologist using a radiotherapy planning system, and the resulting binary segmentation masks were converted into NIfTI format. All data were de-identified prior to release, and facial features were removed from imaging data using a defacing procedure to ensure patient privacy. The dataset represents a valuable resource for the development and validation of computer-aided detection and segmentation methods, including those based on machine learning. A particular strength is the inclusion of small metastatic lesions, which are clinically relevant yet challenging to detect and segment automatically. We anticipate that this collection will facilitate reproducible research and support the development of robust algorithms for clinical translation in radiotherapy planning.
SN  - 2052-4463
ER  -