Datasets:

MedOtter
/

BraTS-TCGA

license: cc-by-3.0
tags:
  - medical
  - brain-mri
  - glioma
  - tumor-segmentation
  - tcia
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: patient_id
      dtype: string
    - name: cohort
      dtype: string
    - name: gt_tier
      dtype: string
    - name: num_slices
      dtype: int32
    - name: shape
      dtype: string
    - name: image
      dtype: image
    - name: mask
      dtype: image
    - name: overlay
      dtype: image
  splits:
    - name: train
      num_bytes: 6186343
      num_examples: 167
  download_size: 6186175
  dataset_size: 6186343

BraTS-TCGA (BraTS-TCGA-GBM + BraTS-TCGA-LGG)

Expert segmentation labels for the pre-operative TCGA glioma MRI cohorts (Bakas et al. 2017), combining the two TCIA analysis-result collections BraTS-TCGA-GBM (102 glioblastoma patients) and BraTS-TCGA-LGG (65 lower-grade glioma patients) = 167 cases.

What this is (faithful-naming note): the publicly released training half of the pre-operative subset of TCGA-GBM / TCGA-LGG, already co-registered to a T1 template, resampled to 1 mm³, and skull-stripped (NIfTI). The 33 GBM + 43 LGG challenge test subjects are withheld by TCIA (controlled access) and are NOT included. The raw DICOM collections (TCGA-GBM / TCGA-LGG) are separate, NIH-controlled, and not mirrored here.

Structure

dataset/{gbm|lgg}/TCGA-XX-XXXX/
  TCGA-XX-XXXX_<date>_t1.nii.gz
  TCGA-XX-XXXX_<date>_t1Gd.nii.gz
  TCGA-XX-XXXX_<date>_t2.nii.gz
  TCGA-XX-XXXX_<date>_flair.nii.gz
  TCGA-XX-XXXX_<date>_GlistrBoost.nii.gz                    (automated)
  TCGA-XX-XXXX_<date>_GlistrBoost_ManuallyCorrected.nii.gz  (when present)
train.jsonl              # one record per case; `mask` = recommended GT
TCGA_GBM_radiomicFeatures.csv
TCGA_LGG_radiomicFeatures.csv

Labels (BraTS convention)

value	structure
1	necrotic + non-enhancing tumor core (NCR/NET)
2	peritumoral edema (ED)
4	GD-enhancing tumor (ET)

Some LGG tumors do not enhance — label 4 legitimately absent in those cases (class absent, not an empty/broken mask).

Ground truth tier

Two masks per case: GlistrBoost (automated, BraTS'15-winning method) and GlistrBoost_ManuallyCorrected (revised and approved by a board-certified neuroradiologist). Recommended GT = ManuallyCorrected when present (97/102 GBM, 62/65 LGG); for the 8 cases without it the automated mask was accepted as-is. train.jsonl field mask already applies this rule (gt_tier records which file was chosen). Known quirk: the corrected files can carry slightly different NIfTI headers than the images — take geometry from the image volume.

Splits

No internal split — this release is the BraTS 2017 training portion only (single split: train).

⚠️ Benchmark overlap

All 167 subjects were folded into the BraTS challenge training data (2017 onward) — do not treat this set as independent of models trained on BraTS (e.g. BraTS2023-GLI). The same TCGA-LGG patients also appear in the 2D TCGA-LGG-Mask dataset. Folder names are TCGA patient barcodes (TCGA-XX-XXXX) — use them for cross-referencing/deduplication; the BraTS name-mapping CSV distributed with BraTS'17–'20 training archives links barcodes to BraTS subject IDs.

License & citation

CC BY 3.0. Cite:

Bakas S, et al. "Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features." Nature Scientific Data 4:170117 (2017). DOI: 10.1038/sdata.2017.117
Data DOIs: GBM 10.7937/K9/TCIA.2017.KLXWJJ1Q, LGG 10.7937/K9/TCIA.2017.GJQ7R0EF
Clark K, et al. "The Cancer Imaging Archive (TCIA)." J Digit Imaging 26(6):1045-1057 (2013).