--- license: cc-by-nc-4.0 dataset_info: config_name: preview features: - name: case_id dtype: string - name: split dtype: string - name: center dtype: string - name: frame_idx dtype: int64 - name: n_frames dtype: int64 - name: height dtype: int64 - name: width dtype: int64 - name: field_strength_t dtype: float64 - name: frame_rate_hz dtype: float64 - name: scanned_region dtype: string - name: has_observer2 dtype: bool - name: image dtype: image - name: mask dtype: image - name: overlay dtype: image splits: - name: train num_bytes: 11460156 num_examples: 88 download_size: 11464593 dataset_size: 11460156 configs: - config_name: preview data_files: - split: train path: preview/train-* tags: - medical - segmentation - mri - cine-mri - radiotherapy - tumor-tracking - video --- # TrackRAD2025 — labeled subsets Real-time tumour tracking for **MRI-guided radiotherapy**: 2D+t sagittal **cine-MRI** acquired on two MR-Linac platforms, with a **single binary target** contoured on **every frame**. This is a mirror of the **labeled** portion of the official [`LMUK-RADONC-PHYS-RES/TrackRAD2025`](https://huggingface.co/datasets/LMUK-RADONC-PHYS-RES/TrackRAD2025) release (DOI [`10.57967/hf/4539`](https://doi.org/10.57967/hf/4539)). ## Scope — read this first The upstream repo is **269 GB**, of which **268 GB is the `unlabeled_training_data` pool (477 patients)**. Those cases ship **no `targets/` directory at all** and carry zero segmentation ground truth, so they are excluded here. This mirror is the **1.0 GB labeled portion**, which is the complete usable set for segmentation: | Split | Upstream subset | Patients | Frames | Size | |---|---|---|---|---| | `train` | `trackrad2025_labeled_training_data` | 50 (A25 / B15 / C10) | 5,027 | 576 MB | | `val` | `trackrad2025_labeled_pre-testing_data` | 8 (A2 / B3 / C3) | 607 | 74 MB | | `test` | `trackrad2025_labeled_testing_data` | 30 (A5 / B8 / C11 / X6) | 2,298 | 324 MB | | **Total** | | **88** | **7,932** | **976 MB** | `val` is the challenge's own "pre-testing" set — the 8-case public validation cohort used during the pre-test phase. Splits are the **official challenge splits**, preserved as-is. **Cohort D (`D_001`–`D_020`) is permanently withheld** by the organisers for privacy. Public = 88 of the paper's 108 labeled patients; the missing 20 are exactly Cohort D. ## Acquisition | | | |---|---| | Modality | 2D+t sagittal cine-MRI | | Scanners | **0.35 T ViewRay MRIdian** (bSSFP, 4 / 8 Hz) — 32 cases · **1.5 T Elekta Unity** (bFFE, 1.3–3.5 Hz) — 56 cases | | Regions | abdomen 47 · thorax 27 · pelvis 14 | | Centers | 6, letter-anonymised A–F | | Image dtype | `uint16`, resampled to 1 × 1 mm in-plane | | Mask dtype | `uint8`, strictly `{0, 1}` — **1 class** | | Frames / case | 44 – 248 (median 97) | The target is normally the **GTV**, but where tumour contrast was clinically too low the organisers contoured a **surrogate structure instead** (e.g. the whole liver in place of a low-contrast liver lesion). Treat the class as "the tracked structure", not strictly "tumour". ## Layout ``` {subset}/{case_id}/images/{case_id}_frames.mha uint16 (H, W, T) {subset}/{case_id}/targets/{case_id}_labels.mha uint8 (H, W, T) <- ground truth {subset}/{case_id}/targets/{case_id}_first_label.mha uint8 (H, W, 1) <- input prompt {subset}/{case_id}/targets/{case_id}_labels2.mha uint8 (H, W, T) <- 2nd observer, 14 cases {subset}/{case_id}/b-field-strength.json 0.35 | 1.5 {subset}/{case_id}/frame-rate.json Hz {subset}/{case_id}/scanned-region.json thorax | abdomen | pelvis train.jsonl val.jsonl test.jsonl per-case manifests (repo-relative paths) ``` Note the file is **`b-field-strength.json`**, not `field-strength.json` — the upstream card's folder diagram has this wrong. ## Gotchas — all verified against all 88 cases 1. **Axis order is `(H, W, T)` — time is the LAST numpy axis.** `SimpleITK`'s `GetSpacing()` returns `(5.0, 1.0, 1.0)`, and that leading 5 mm is **slice thickness parked on the time axis** — an artifact of writing 2D+t as a 3D volume. Do **not** read it as a 3D volume with 5 mm z-spacing. The official evaluator does `transpose(2, 0, 1)` to reach `(T, H, W)`. 2. **Ground truth is DENSE, not sparse.** Despite being a tracking challenge, every frame is labeled: `T(labels) == T(frames)` in **88/88 cases, zero mismatches**. 3. **`_first_label.mha` is byte-identical to `labels[..., 0]`** (verified 88/88). It is the algorithm's *input prompt* (Grand Challenge interface `mri-linac-target`), **not an extra annotated frame**. The dataset paper's phrase "the labels of the *remaining* frames" is wrong — concatenating `first_label` onto `labels` yields an off-by-one `T+1`. 4. **In-plane size is ragged across cases**: 270, 350, 423, 424, 425, 426, 437, 450 (all square). Do not `np.array()` a mixed batch. 5. **Labels are already `{0, 1}`.** No min-max normalisation or `> 0.5` threshold — a binarisation recipe applied here is a no-op at best. 6. **3 cases contain empty frames** (target out of plane): `A_013` 14/100, `A_018` 12/100, `B_018` **35/70**. 7,871 of 7,932 frames have foreground. The official evaluator excludes empty-GT frames from metrics. ## Ground-truth tiers | File | Role | Coverage | |---|---|---| | `_labels.mha` | Primary observer, per-frame | **all 88 — use this** | | `_labels2.mha` | Second independent observer | 14 cases, **all Center C** (655 frames) | | `_staple_labels.mha` | Official gold standard | **not distributed by the organisers** | The official `evaluation/evaluate.py` reads `_labels.mha` and then *overrides* it with `_staple_labels.mha` where present — but the STAPLE files were never released. To match official scoring on the 14 two-observer cases you must recompute STAPLE yourself from `labels` + `labels2`; this is not cosmetic (measured observer-1 vs observer-2 DSC on `C_016` = **0.871**). This mirror therefore uses **`_labels.mha` uniformly** so the GT tier is consistent across all 88 cases, and ships `labels2` alongside for anyone who wants to reconstruct STAPLE. Center D used **5 observers**, which is the source of the paper's "+8000 multi-observer frames" — but D is withheld, so only **~655** multi-observer frames are actually public. ## Grouping / leakage Group on **`case_id`**; each case is one continuous cine sequence. ⚠️ **Cohort X is not provably independent of B/C.** `X_001`–`X_006` (test split) are Elekta CMM-sequence acquisitions drawn from one of the source centers A–D — necessarily **B or C**, since X is 1.5 T and labeled. Whether they are the *same patients* as some `B_*` / `C_*` cases is unstated upstream. If you re-split away from the official splits, treat X as potentially non-independent from B and C. ## Provenance Official organiser release, DOI-minted, no third-party re-host. Counts reconcile exactly: 585 = 477 unlabeled + 50 train + 8 pre-test + 50 test; public = 585 − 20 (Cohort D). The upstream data is itself a **preprocessed variant** — resampled to 1 × 1 mm, reoriented, with the first 5 frames (0.35 T) / 3 frames (1.5 T) dropped to reach steady state plus any leading frames where the target was not yet visible (so frame 0 always contains the target). **Sagittal plane only**: 1.5 T acquisitions were interleaved sagittal/coronal(/axial) and only the sagittal series is included. No documented patient overlap with any other public dataset, and no cross-reference ID column exists (`case_id` is `
_<3 digits>`). ## License **CC BY-NC 4.0** — as stated in the dataset paper §2.3 and the upstream card. Note the *article* is CC BY 4.0; that applies to the text, not the data. ## Citation ```bibtex @article{trackrad2025data, title = {TrackRAD2025 challenge dataset: real-time tumor tracking for MRI-guided radiotherapy}, author = {Wang, Yiling and Lombardo, Elia and Thummerer, Adrian and Bl{\"o}cker, Tom and Maspero, Matteo and others}, journal = {Medical Physics}, volume = {52}, number = {7}, pages = {e17964}, year = {2025}, doi = {10.1002/mp.17964} } @article{trackrad2025challenge, title = {MRIgRT real-time target tracking: TrackRAD2025 challenge report}, author = {Bl{\"o}cker, Tom J. and G{\"o}rts, Pim A. W. and Wang, Yiling and Lombardo, Elia and Landry, Guillaume and others}, journal = {Medical Image Analysis}, volume = {112}, pages = {104134}, year = {2026}, doi = {10.1016/j.media.2026.104134} } ```