| --- |
| 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. |
|
|
