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Open Imaging Index
MRI Brain

AIMS-TBI

Automated Identification of Moderate-Severe Traumatic Brain Injury Lesions (AIMS-TBI) challenge dataset

T1-weighted brain MRI after moderate to severe traumatic brain injury in children and adults, with manual lesion masks, released for a MICCAI lesion segmentation challenge. 892 defaced scans from 13 sites (2025 release), shared after a data use agreement.

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Overview

AIMS-TBI is a lesion segmentation challenge on brain MRI after moderate to severe traumatic brain injury, run at MICCAI in 2024, 2025 and 2026 by researchers at the University of Utah. The images come from the paediatric and adult moderate-severe TBI working groups of the ENIGMA consortium. The task is to detect and segment lesions on a single T1-weighted scan, the sequence most sites have.

Composition

The 2025 dataset holds 892 T1-weighted images: 553 for training, 100 for validation and 239 for a hidden test set, drawn from 13 sites with a similar mix of sites in each split. A case is one scan timepoint, and some patients have longitudinal scans, so the number of people is lower than the number of images and is not reported. The 2024 edition used 764 images (388 training, 101 validation, 275 test). For 2026 the same data were reused, and some training cases also come with diffusion MRI, T2-weighted, FLAIR or SWI scans, without separate lesion masks. Age, sex and time since injury come with the images. The source does not say whether the working groups contributed control subjects.

Acquisition

Scans were made on GE, Siemens and Philips scanners at 1.5 T and 3 T with differing protocols. Most T1-weighted images have 1 mm isotropic voxels. Contributing sites include Baylor College of Medicine, UCLA, Penn State, Kennedy Krieger Institute, Loma Linda, Nationwide Children's Hospital, Murdoch Children's Research Institute, Deakin University, UT Houston, Kessler Foundation, VA Palo Alto and the University of Oslo. Images are defaced with pydeface and carry no other preprocessing.

Annotations

Lesions are binary masks that merge all visible TBI damage, such as contusions, haemorrhage, haematoma, encephalomalacia, gliosis, white matter lesions and drainage tracts. Each mask went through three steps: a trained rater edited an automated segmentation, a second rater reviewed it, and one of five expert raters approved it. Raters had to reach a Dice of 0.6 on training cases before working on real data. Masks are released for training cases only.

Known limitations

  • Access requires a data use agreement with the University of Utah; registration for the 2026 edition is closed.
  • One lesion class: haemorrhage is not separated from other lesion types.
  • Subject counts, age and sex distributions per split are not published.
  • Two terms apply: the signed agreement and a CC BY-NC-ND license named in the challenge design document.

Cohort

Aggregate numbers from the sources below. Bars are relative to the largest value.

Contrast / sequence

Scans

Groups can overlap

  • T1-weighted 892 100%

Training and test case characteristics (c) in Dennis et al. 2026, AIMS-TBI structured challenge design document (Zenodo, CC BY 4.0)

Split

Scans
  • Training 553 62%
  • Test 239 27%
  • Validation 100 11%

Training and test case characteristics (b) in Dennis et al. 2026, AIMS-TBI structured challenge design document (Zenodo, CC BY 4.0)

License and access

Our reading of the license, not legal advice. Before you use the data, read the original license and confirm that your use is allowed. We take no responsibility for how you use a dataset. Full disclaimer

Access
Signed agreement

Sign a data use agreement, often reviewed by the provider

Access page

Different parts of the data carry different licenses. The summary on the right shows the most restrictive answer per rule.

For All challenge data (T1w MRI, lesion masks and demographics), under the agreement signed in the data access form.

AIMS-TBI Data Use Agreement (University of Utah)

Signed through the challenge data access form with the University of Utah. Use is limited to research under the University of Utah IRB 138479 or other purposes agreed in writing; the data may not be passed on, publications must credit the University of Utah, and the data must be returned or destroyed when the permitted use ends.

Original license text Version read: Agreement text in the "AIMS-TBI data access 2026" Google Form, read 2026-10-08 (the 2025 form has the same text) Checked 2026-10-08

What you can do

  • Conditional
  • Not stated
  • Not stated
  • Create derived data Not stated
  • Conditional

What you can share

  • No
  • Not stated
  • Share trained models Not stated

What you must do

  • Yes
  • Share alike No
  • Yes
  • No
  • Manuscript review No
  • Release code No
  • Return results No
  • Yes

Limits

  • No re-identification No
  • No
For All challenge data, as declared in the "Data usage agreement" section of the 2026 challenge design document. The document says "CC BY-NC-ND" without a version.

Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Share the unchanged data with credit for non-commercial purposes. You may not share anything derived from it, and Creative Commons advises against training models on it.

Original license text Version read: 4.0 Checked 2026-10-07

What you can do

  • No
  • Not stated
  • No
  • Conditional
  • Yes

What you can share

  • Conditional
  • No
  • No

What you must do

  • Yes
  • Share alike No
  • No
  • No
  • Manuscript review No
  • Release code No
  • Return results No
  • Delete after use No

Limits

  • No
  • Location limits No

Citation

Deutscher E, Tustison N, Onicas A, Wilde EA, Pease MW, Bakas S, Dennis EL. Automated Identification of Moderate-to-Severe Traumatic Brain Injury Lesions (AIMS-TBI) 2025 MICCAI Challenge. Lecture Notes in Computer Science, Springer (2026). doi:10.1007/978-3-032-16370-7_24

Sources

Every number on this page comes from one of these documents. Each chart names the table or page it is taken from. The raw numbers are in stats.csv.