---
license: cc-by-nc-sa-4.0
pretty_name: VALDO — Vascular Lesions Detection and Segmentation (MICCAI 2021)
task_categories:
- image-segmentation
tags:
- medical-imaging
- mri
- brain
- cerebral-small-vessel-disease
- perivascular-spaces
- cerebral-microbleeds
- lacunes
- neuroimaging
size_categories:
- n<1K
dataset_info:
  features:
  - name: task
    dtype: string
  - name: subject_id
    dtype: string
  - name: cohort
    dtype: string
  - name: lesion_type
    dtype: string
  - name: reference_space
    dtype: string
  - name: has_segmentation
    dtype: bool
  - name: n_rater_masks
    dtype: int32
  - name: gt_rule
    dtype: string
  - name: mask_voxels
    dtype: int64
  - name: slice_idx
    dtype: int32
  - name: num_slices
    dtype: int32
  - name: ref_other_name
    dtype: string
  - name: image_T1
    dtype: image
  - name: image_T2
    dtype: image
  - name: image_ref_other
    dtype: image
  - name: mask
    dtype: image
  - name: overlay
    dtype: image
  splits:
  - name: task1_epvs
    num_bytes: 9742157
    num_examples: 40
  - name: task2_cmb
    num_bytes: 13932473
    num_examples: 72
  - name: task3_lacunes
    num_bytes: 9849342
    num_examples: 40
  download_size: 33549282
  dataset_size: 33523972
configs:
- config_name: default
  data_files:
  - split: task1_epvs
    path: data/task1_epvs-*
  - split: task2_cmb
    path: data/task2_cmb-*
  - split: task3_lacunes
    path: data/task3_lacunes-*
---

# VALDO — "Where is VALDO?" VAscular Lesions DetectiOn and segmentatiOn challenge (MICCAI 2021)

> **⚠️ This repository is the publicly-released *training set only* of VALDO 2021.**
> The challenge had **431 cases total (152 train + 279 test)**; the **279-case test set was never publicly
> distributed** (the organisers evaluate submitted Docker containers on a held-out server). What you see here is
> the **152-case training release** from Zenodo — the only portion the authors made openly downloadable.
>
> **⚠️ Preprocessed-for-challenge variant, not raw acquisitions.** All images are **defaced**, and within each
> task the non-reference sequences are **rigidly/affinely co-registered and resampled** to the task's reference
> space (T1 for Tasks 1 & 3, T2\* for Task 2).

VALDO targets three imaging markers of **cerebral small vessel disease (CSVD)** on brain MRI, organised as three
independent tasks. Data is drawn from three population cohorts: **SABRE** (London), **RSS = Rotterdam Scan Study**
(part of the Rotterdam Study), and **ALFA** (BarcelonaBeta, Task 2 only).

## Tasks & contents

Layout is BIDS-like: `Task{1,2,3}/sub-XXX/` with files named
`sub-{ID}_space-{Space}_desc-{Description}_{Suffix}.nii.gz`.

### Task 1 — Enlarged Perivascular Spaces (EPVS) · 40 subjects · T1 space
| Suffix | Role |
|---|---|
| `desc-masked_T1` / `desc-masked_T2` / `desc-masked_FLAIR` | images (T2/FLAIR co-registered to T1, defaced) |
| `desc-masked_Regions` | region mask: each region a distinct integer, keyed to `Count.csv` |
| `Count.csv` | per-region EPVS counts (`value, count, min_slice, max_slice`; slices 0-indexed) |
| `desc-Rater{1,2}_PVSSeg` / `PVSSeg` | EPVS segmentation — **present for only 12 of 40 subjects** |

> **Heterogeneous / weak-label task.** Of 40 subjects: **6 (SABRE, sub-101…106)** have voxel segmentations from
> **two raters** (`desc-Rater1/Rater2_PVSSeg`); **6 (RSS)** have a **single** `PVSSeg`; the remaining **28 (RSS)**
> have **counts only** (`Count.csv` + `Regions`, **no segmentation mask**). A segmentation loader must filter to
> the 12 subjects with a `PVSSeg` file; the 28 counts-only cases are a deliberate weak-supervision tier.

### Task 2 — Cerebral Microbleeds (CMB) · 72 subjects · T2\* space
| Suffix | Role |
|---|---|
| `desc-masked_T2S` / `desc-masked_T2` / `desc-masked_T1` | images (T2/T1 co-registered to T2\*, defaced) |
| `CMB` | microbleed segmentation — **70 of 72 subjects** carry a mask |

> 2 subjects have no `CMB` file (treat as zero-microbleed / empty-mask cases). Cohorts: SABRE, RSS, ALFA.

### Task 3 — Lacunes · 40 subjects · T1 space
| Suffix | Role |
|---|---|
| `desc-masked_T1` / `desc-masked_T2` / `desc-masked_FLAIR` | images (T2/FLAIR co-registered to T1, defaced) |
| `desc-Rater{1,2,3,4}_Lacunes` | lacune segmentation — **two raters per subject** |

> **Every** subject has exactly **two** rater masks, but the rater *identity differs by cohort*:
> **SABRE (sub-101…106)** uses `Rater1`+`Rater2`; **RSS (sub-201+)** uses `Rater3`+`Rater4`. A loader must pick
> whichever two `*_Lacunes` files exist per subject — do **not** hardcode `Rater1`/`Rater2`. One subject
> (`sub-105`) additionally carries a `sub-105_space-T1_possible.nii.gz` ("possible lacunes") auxiliary mask.

## Gold-standard ground truth (paper's consensus rule)

Where **two rater masks** exist (Task 3 all cases; Task 1 SABRE sub-101…106), the reference standard is the
**average of the two raters' masks, thresholded at 0.5** (binary). Where a single mask exists (Task 1 RSS single
`PVSSeg`; Task 2 `CMB`), that mask is the ground truth. **All rater masks are preserved here** so this rule can be
applied — or revised — downstream.

## ⚠️ Cross-cohort lineage (leakage note)

VALDO subjects come from **SABRE**, **RSS (Rotterdam Scan Study)**, and **ALFA** population cohorts. There is **no
overlap** with tumor/oncology benchmarks (BraTS family, Medical Segmentation Decathlon, TCIA collections). The one
lineage to watch is **RSS / the Rotterdam Study**, which contributes to many neuroimaging datasets — de-duplicate
before combining VALDO with any other Rotterdam-derived set. **No cross-reference ID column is exposed**: subject
IDs are challenge-local (`sub-###`) and original cohort IDs are not released, so subject-level linking is not
directly possible from these files.

## License

The Zenodo record's metadata states **CC BY-NC 4.0**, while the paper and the original README state **CC BY-NC-SA**
(NonCommercial-**ShareAlike**). We adopt the **more restrictive non-commercial + share-alike** interpretation
(`cc-by-nc-sa-4.0`). Either way the data is **non-commercial use only**.

## Provenance

Official author release — Zenodo record **10.5281/zenodo.4520773** (the challenge organisers' own deposit, linked
from https://valdo.grand-challenge.org/). Public training counts (Task1=40, Task2=72, Task3=40 → 152) match the
paper's Table 2 exactly. Not a third-party re-host. Re-distributed here under the source non-commercial license.

## Citation

Sudre, C.H., Van Wijnen, K., Dubost, F., et al. *"Where is VALDO? VAscular Lesions Detection and segmentatiOn
challenge at MICCAI 2021."* Medical Image Analysis, 91:103029, 2024. https://doi.org/10.1016/j.media.2023.103029
(preprint arXiv:2208.07167). Challenge: https://valdo.grand-challenge.org/ · Data: https://zenodo.org/records/4520773