--- 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.