TrackRAD2025 / README.md
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---
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 `<center letter>_<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}
}
```