README.md

---
license: cc-by-4.0
task_categories:
- image-segmentation
tags:
- medical
- brain
- brain-metastases
- mri
- 3d
- radiotherapy
- stereotactic-radiosurgery
pretty_name: BEAMSTER (brain metastases for stereotactic radiotherapy)
configs:
- config_name: preview
  data_files:
  - split: train
    path: preview/train-*
dataset_info:
  config_name: preview
  features:
  - name: case_id
    dtype: string
  - name: subset
    dtype: string
  - name: sex
    dtype: string
  - name: age
    dtype: int32
  - name: scanner
    dtype: string
  - name: primary_origin
    dtype: string
  - name: image_file
    dtype: string
  - name: mask_file
    dtype: string
  - name: num_slices
    dtype: int32
  - name: preview_slice
    dtype: int32
  - name: lesion_volume_cc
    dtype: float32
  - name: n_components
    dtype: int32
  - name: spacing_mm
    list: float32
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: overlay
    dtype: image
  - name: overlay_zoom
    dtype: image
  splits:
  - name: train
    num_bytes: 29643563
    num_examples: 140
  download_size: 29639302
  dataset_size: 29643563
---

# BEAMSTER

**B**rain m**E**t**A**stases seg**M**entation for **STE**reotactic **R**adiotherapy — a
retrospective MRI dataset with expert segmentations. Re-host of the author deposit
[figshare 10.6084/m9.figshare.29365844](https://doi.org/10.6084/m9.figshare.29365844) v1
(**CC BY 4.0**), from University Hospital Ostrava, Czech Republic, Oct 2019 - Sep 2024.

140 patients, one contrast-enhanced T1w 3D MPRAGE volume each, with a binary
brain-metastasis mask drawn by a board-certified radiation oncologist (13 years'
experience) for stereotactic radiotherapy planning and independently verified by a
board-certified neuroradiologist.

## Cohorts

| subset | cases | lesions | mean lesion vol | note |
|---|---|---|---|---|
| `Dataset_A` | 113 | 216 | 6.7 cc | treated Oct 2019 - Apr 2022, 1/3/5 fractions |
| `Dataset_B` | 27 | 44 | 0.5 cc | treated Jul 2021 - Sep 2024, **deliberately enriched with very small lesions** |

There is **no official train/val/test split** — `train.jsonl` carries all 140 rows with
`split: "train"`. `subset` is a cohort label, not a split.

> **Do not use A vs B as a train/test partition.** Their accrual windows overlap by
> 9 months at the same institution, and the paper never states the cohorts are
> patient-disjoint. The de-identified IDs make this unverifiable.

## Layout

```
dataset/Dataset_A/Dataset_A_YYY.nii.gz        ce-T1w volume
dataset/Dataset_A/Dataset_A_YYY_segm.nii.gz   binary metastasis mask
dataset/Dataset_B/...                          same convention
Spreadsheets/Table_clinical_data.{csv,xlsx}    140 rows of clinical data
Spreadsheets/image_voxel_parameters.{csv,xlsx} per-case dimensions + voxel size
README_original.txt                            the deposit's own README, verbatim
train.jsonl                                    one row per case (see columns below)
```

`train.jsonl` columns: `case_id`, `subset`, `image`, `mask`, `shape`, `spacing_mm`,
`orientation`, `lesion_volume_cc`, `n_components`, `fg_voxels`, `split`, plus the
clinical fields `sex`, `age`, `scanner`, `primary_origin`, `rt_dose_gy`, `n_fractions`,
`isodose_pct`, `vital_status`, `survival_days`, `prior_surgery`, `treatment_status`.
`vital_status` (1 case) and `survival_days` (15 cases) are nullable.

## Verified properties

- Image and mask share an **identical shape and affine in all 140 cases** — no
  resampling is needed to pair them.
- Masks are strictly `{0, 1}` but stored as **float32** — cast to uint8 in a loader.
- All volumes are 512x512 in-plane, z = 201-506. **72 distinct voxel spacings**;
  slice thickness is 1.0 mm (137 cases), 1.25 mm (2), 0.625 mm (1).
- Orientation is **LPS for 139 cases and LAS for `Dataset_B_001`**. Image and mask
  agree within every case, so this only matters to code that hardcodes axis order.
- No empty masks. ID sequences have gaps: `Dataset_A` spans 001-123 (10 missing),
  `Dataset_B` spans 001-030 (3 missing).

## Caveats that affect evaluation

1. **Masks are a GTV/PTV mixture.** Contours follow ICRU 50 and the exported structure
   is *"GTV or PTV, depending on availability"* — so an unknown subset of masks carries
   a planning margin and is systematically larger than the tumour itself. **No column in
   any released table records which case got which.**
2. **Annotation is partial by design.** Only lesions *selected for irradiation* were
   contoured. Other metastases visible in the same scan are unlabelled background, so a
   correct detection can be scored as a false positive.
3. **Not native geometry.** Every volume was rigidly registered to the radiotherapy
   planning CT and resampled into **CT coordinate space and resolution**; acquisition was
   0.9-1.0 mm isotropic. Volumes are also AFNI-**defaced**. The planning CT is not released.
4. **Connected components != lesions.** The masks contain 335 26-connected components
   against the paper's 260 lesions, and no size threshold reconciles the two. Total
   segmented *volume* does match the paper (within 4%), so the masks are faithful — but
   instance-level metrics will not reproduce the published lesion counts.
5. **Extreme class imbalance.** 55.8% of components are under 1 cc; the smallest
   foreground fraction is 2.8e-06 (`Dataset_A_074`, ~200 voxels in 75M). Evaluating on a
   single sample will report a near-zero Dice that looks like a bug but is not.
6. **Single observer.** One annotator; the paper explicitly states inter-observer
   variability was not assessed. There are no multi-rater or consensus tiers.

## Overlap with other datasets

None found. Single-institution Czech data; the BraTS-METS 2023, BraTS-METS 2025
Lighthouse and UCSF-BMSR papers contain no reference to Ostrava, Czech sites,
CyberKnife or MultiPlan. No cross-reference ID column exists — IDs are de-identified
`Dataset_X_YYY` only.

## Citation

Nohel M, Reguli S, Kaplanova R, Jackaninova J, Chmelik J, Knybel L. *BEAMSTER: Brain
mEtAstases segMentation for STEreotactic Radiotherapy, A Retrospective MRI Dataset with
Expert Segmentations.* Scientific Data (2026). doi:10.1038/s41597-026-07777-0

Data: doi:10.6084/m9.figshare.29365844 — **CC BY 4.0**, redistribution permitted with
attribution, commercial use permitted.