Datasets:

MedOtter
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BraTS-TCGA

patient_id stringlengths 12 12	cohort stringclasses 2 values	gt_tier stringclasses 2 values	num_slices int32 155 155	shape stringclasses 1 value
TCGA-02-0006	gbm	manually_corrected	155	[240, 240, 155]
TCGA-02-0009	gbm	manually_corrected	155	[240, 240, 155]
TCGA-02-0011	gbm	manually_corrected	155	[240, 240, 155]
TCGA-02-0027	gbm	manually_corrected	155	[240, 240, 155]
TCGA-02-0033	gbm	manually_corrected	155	[240, 240, 155]
TCGA-02-0034	gbm	manually_corrected	155	[240, 240, 155]
TCGA-02-0037	gbm	manually_corrected	155	[240, 240, 155]
TCGA-02-0046	gbm	manually_corrected	155	[240, 240, 155]
TCGA-02-0047	gbm	manually_corrected	155	[240, 240, 155]
TCGA-02-0054	gbm	manually_corrected	155	[240, 240, 155]
TCGA-02-0059	gbm	manually_corrected	155	[240, 240, 155]
TCGA-02-0064	gbm	manually_corrected	155	[240, 240, 155]
TCGA-02-0068	gbm	manually_corrected	155	[240, 240, 155]
TCGA-02-0069	gbm	manually_corrected	155	[240, 240, 155]
TCGA-02-0070	gbm	glistrboost_auto	155	[240, 240, 155]
TCGA-02-0075	gbm	manually_corrected	155	[240, 240, 155]
TCGA-02-0085	gbm	manually_corrected	155	[240, 240, 155]
TCGA-02-0086	gbm	manually_corrected	155	[240, 240, 155]
TCGA-02-0087	gbm	manually_corrected	155	[240, 240, 155]
TCGA-02-0102	gbm	manually_corrected	155	[240, 240, 155]
TCGA-02-0106	gbm	manually_corrected	155	[240, 240, 155]
TCGA-02-0116	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0119	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0122	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0130	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0137	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0138	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0139	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0142	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0145	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0149	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0154	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0158	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0162	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0164	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0176	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0177	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0179	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0182	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0184	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0185	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0187	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0188	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0190	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0192	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0238	gbm	glistrboost_auto	155	[240, 240, 155]
TCGA-06-0240	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0644	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-0646	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-1084	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-1802	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-2570	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-5408	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-5413	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-5417	gbm	manually_corrected	155	[240, 240, 155]
TCGA-06-6389	gbm	manually_corrected	155	[240, 240, 155]
TCGA-08-0355	gbm	manually_corrected	155	[240, 240, 155]
TCGA-08-0356	gbm	manually_corrected	155	[240, 240, 155]
TCGA-08-0359	gbm	manually_corrected	155	[240, 240, 155]
TCGA-08-0360	gbm	manually_corrected	155	[240, 240, 155]
TCGA-08-0385	gbm	manually_corrected	155	[240, 240, 155]
TCGA-08-0389	gbm	manually_corrected	155	[240, 240, 155]
TCGA-08-0390	gbm	manually_corrected	155	[240, 240, 155]
TCGA-08-0392	gbm	manually_corrected	155	[240, 240, 155]
TCGA-08-0509	gbm	glistrboost_auto	155	[240, 240, 155]
TCGA-08-0512	gbm	manually_corrected	155	[240, 240, 155]
TCGA-08-0520	gbm	glistrboost_auto	155	[240, 240, 155]
TCGA-08-0522	gbm	manually_corrected	155	[240, 240, 155]
TCGA-12-0616	gbm	manually_corrected	155	[240, 240, 155]
TCGA-12-0776	gbm	manually_corrected	155	[240, 240, 155]
TCGA-12-0829	gbm	manually_corrected	155	[240, 240, 155]
TCGA-12-1094	gbm	manually_corrected	155	[240, 240, 155]
TCGA-12-1098	gbm	manually_corrected	155	[240, 240, 155]
TCGA-12-1598	gbm	manually_corrected	155	[240, 240, 155]
TCGA-12-1601	gbm	manually_corrected	155	[240, 240, 155]
TCGA-12-3650	gbm	glistrboost_auto	155	[240, 240, 155]
TCGA-14-1456	gbm	manually_corrected	155	[240, 240, 155]
TCGA-14-1794	gbm	manually_corrected	155	[240, 240, 155]
TCGA-14-1825	gbm	manually_corrected	155	[240, 240, 155]
TCGA-14-3477	gbm	manually_corrected	155	[240, 240, 155]
TCGA-19-0963	gbm	manually_corrected	155	[240, 240, 155]
TCGA-19-1789	gbm	manually_corrected	155	[240, 240, 155]
TCGA-19-2624	gbm	manually_corrected	155	[240, 240, 155]
TCGA-19-2631	gbm	manually_corrected	155	[240, 240, 155]
TCGA-19-5951	gbm	manually_corrected	155	[240, 240, 155]
TCGA-19-5954	gbm	manually_corrected	155	[240, 240, 155]
TCGA-19-5958	gbm	manually_corrected	155	[240, 240, 155]
TCGA-19-5960	gbm	manually_corrected	155	[240, 240, 155]
TCGA-76-4932	gbm	manually_corrected	155	[240, 240, 155]
TCGA-76-4934	gbm	manually_corrected	155	[240, 240, 155]
TCGA-76-4935	gbm	manually_corrected	155	[240, 240, 155]
TCGA-76-6191	gbm	manually_corrected	155	[240, 240, 155]
TCGA-76-6193	gbm	manually_corrected	155	[240, 240, 155]
TCGA-76-6280	gbm	manually_corrected	155	[240, 240, 155]
TCGA-76-6282	gbm	manually_corrected	155	[240, 240, 155]
TCGA-76-6285	gbm	manually_corrected	155	[240, 240, 155]
TCGA-76-6656	gbm	manually_corrected	155	[240, 240, 155]
TCGA-76-6657	gbm	manually_corrected	155	[240, 240, 155]
TCGA-76-6661	gbm	manually_corrected	155	[240, 240, 155]
TCGA-76-6662	gbm	manually_corrected	155	[240, 240, 155]

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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).

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