Datasets:
task stringclasses 1
value | subject_id stringlengths 7 7 | cohort stringclasses 2
values | lesion_type stringclasses 1
value | reference_space stringclasses 1
value | has_segmentation bool 2
classes | n_rater_masks int32 0 2 | gt_rule stringclasses 3
values | mask_voxels int64 0 7.41k | slice_idx int32 96 140 | num_slices int32 192 256 | ref_other_name stringclasses 1
value | image_T1 imagewidth (px) 180 512 | image_T2 imagewidth (px) 180 512 | image_ref_other imagewidth (px) 180 512 | mask imagewidth (px) 180 512 | overlay imagewidth (px) 180 512 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Task1 | sub-101 | SABRE | EPVS | T1 | true | 2 | avg2raters@0.5 | 3,718 | 129 | 256 | FLAIR | |||||
Task1 | sub-102 | SABRE | EPVS | T1 | true | 2 | avg2raters@0.5 | 7,406 | 128 | 256 | FLAIR | |||||
Task1 | sub-103 | SABRE | EPVS | T1 | true | 2 | avg2raters@0.5 | 2,846 | 129 | 256 | FLAIR | |||||
Task1 | sub-104 | SABRE | EPVS | T1 | true | 2 | avg2raters@0.5 | 997 | 120 | 256 | FLAIR | |||||
Task1 | sub-105 | SABRE | EPVS | T1 | true | 2 | avg2raters@0.5 | 4,860 | 132 | 256 | FLAIR | |||||
Task1 | sub-106 | SABRE | EPVS | T1 | true | 2 | avg2raters@0.5 | 5,015 | 128 | 256 | FLAIR | |||||
Task1 | sub-201 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-202 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-203 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-204 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-205 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-206 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-207 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-208 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-209 | RSS | EPVS | T1 | true | 1 | single_mask | 332 | 137 | 192 | FLAIR | |||||
Task1 | sub-210 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-211 | RSS | EPVS | T1 | true | 1 | single_mask | 1,429 | 140 | 192 | FLAIR | |||||
Task1 | sub-212 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-213 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-214 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-215 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-216 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-217 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-218 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-219 | RSS | EPVS | T1 | true | 1 | single_mask | 1,318 | 137 | 192 | FLAIR | |||||
Task1 | sub-220 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-221 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-222 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-223 | RSS | EPVS | T1 | true | 1 | single_mask | 974 | 128 | 192 | FLAIR | |||||
Task1 | sub-224 | RSS | EPVS | T1 | true | 1 | single_mask | 370 | 122 | 192 | FLAIR | |||||
Task1 | sub-225 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-226 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-227 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-228 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-229 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-230 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-231 | RSS | EPVS | T1 | true | 1 | single_mask | 465 | 133 | 192 | FLAIR | |||||
Task1 | sub-232 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-233 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR | |||||
Task1 | sub-234 | RSS | EPVS | T1 | false | 0 | counts_only(no_seg) | 0 | 96 | 192 | FLAIR |
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 singlePVSSeg; the remaining 28 (RSS) have counts only (Count.csv+Regions, no segmentation mask). A segmentation loader must filter to the 12 subjects with aPVSSegfile; 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
CMBfile (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+) usesRater3+Rater4. A loader must pick whichever two*_Lacunesfiles exist per subject — do not hardcodeRater1/Rater2. One subject (sub-105) additionally carries asub-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
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