UPENN-GBM — mpMRI + Tumor Segmentation

The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: structural multi-parametric MRI (mpMRI) of de novo glioblastoma patients with tumor sub-region segmentations. This is the NIfTI release from TCIA — images are skull-stripped and co-registered to the SRI24 atlas, and the segmentations are aligned to them.

This HuggingFace mirror is a segmentation-focused subset of the full TCIA collection. It contains the structural mpMRI sequences and the tumor segmentations only. The DICOM package (139 GB), the DSC/DTI derivative maps, the skull-unstripped images, the histopathology WSIs (149 GB), and the radiomic-feature tables are not included here — obtain those from TCIA.

Dataset Details

Field	Value
Modality	Brain mpMRI — T1, T1-Gd (T1CE), T2, T2-FLAIR
Body part	Brain (de novo glioblastoma)
Task	3D multi-class tumor sub-region segmentation
Structural scans	671 (630 patients; _21 = follow-up timepoints)
Manual/expert masks	147 (images_segm) — recommended ground truth
Automated masks	611 (automated_segm)
Segmentable scans	611 (have a manual and/or automated mask)
Volume geometry	240 × 240 × 155, 1 mm isotropic, SRI24 atlas space
Format	NIfTI (.nii.gz)
License	CC BY 4.0

Label Scheme (BraTS convention)

Value	Tumor sub-region
0	Background
1	Necrotic / non-enhancing tumor core (NCR/NET)
2	Peritumoral edematous / infiltrated tissue (ED)
4	GD-enhancing tumor (ET)

Evaluation regions: WT (whole tumor) = 1+2+4, TC (tumor core) = 1+4, ET (enhancing tumor) = 4. Note the enhancing-tumor label is 4 (native BraTS/TCIA encoding), not 3 — verified across the released masks.

Mask Sources (two)

1. images_segm — manually-corrected expert segmentation (147 scans). Automated labels reviewed and corrected/approved by board-certified neuroradiologists. This is the recommended ground truth.
2. automated_segm — automated segmentation (611 scans). Label fusion (STAPLE) of an ensemble of top BraTS-ranked deep models (DeepMedic, DeepSCAN, nnU-Net). Silver/weak standard.

All 147 manually-corrected scans also have an automated mask. 60 scans (follow-up _21 timepoints) have neither and are image-only.

Recommended GT policy (used by subjects_manifest.json): prefer the manual mask in images_segm; fall back to automated_segm; skip scans with neither.

Structure

images_structural/<subject>/<subject>_T1.nii.gz
images_structural/<subject>/<subject>_T1GD.nii.gz
images_structural/<subject>/<subject>_T2.nii.gz
images_structural/<subject>/<subject>_FLAIR.nii.gz
images_segm/<subject>_segm.nii.gz                          # manual/expert GT (147)
automated_segm/<subject>_automated_approx_segm.nii.gz      # automated (611)
metadata/UPENN-GBM_clinical_info_v2.1.csv                  # clinical + genomic per subject
subjects_manifest.json                                     # per-scan paths, mask availability, GT policy

<subject> = UPENN-GBM-NNNNN_TT, where TT is the timepoint (11 = baseline, 21 = follow-up). subjects_manifest.json lists, for every structural scan, the four modality paths, the manual/automated mask paths (if present), and the chosen gt_path/gt_source — so loaders need not re-derive availability.

Notes for Loaders

• Images and masks share an identical grid (240×240×155, 1 mm iso, SRI24) — no resampling or axis permutation is needed between a scan and its mask.
• The NIfTI images are SRI-registered and do not align with the TCIA DICOM package by design.
• Multi-channel input: stack T1/T1GD/T2/FLAIR as channels (BraTS-style).

Source

• TCIA collection: https://www.cancerimagingarchive.net/collection/upenn-gbm/
• DOI: 10.7937/TCIA.709X-DN49
• Public, no registration required (TCIA fully public since 2025-07-07).

Citation

@article{bakas2022upenngbm,
  author  = {Bakas, Spyridon and Sako, Chiharu and Akbari, Hamed and Bilello, Michel
             and Sotiras, Aristeidis and Shukla, Gaurav and Rudie, Jeffrey D. and
             Flores Santamar\'ia, Nadina and Fathi Kazerooni, Anahita and Pati, Sarthak
             and others},
  title   = {The University of Pennsylvania glioblastoma (UPenn-GBM) cohort:
             advanced MRI, clinical, genomics, \& radiomics},
  journal = {Scientific Data},
  volume  = {9},
  number  = {1},
  pages   = {453},
  year    = {2022},
  doi     = {10.1038/s41597-022-01560-7}
}