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UPENN-GBM

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Image Segmentation
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Tags:
medical
mri
mpmri
brain
glioblastoma
glioma
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metadata 
license: cc-by-4.0
task_categories:
  - image-segmentation
tags:
  - medical
  - mri
  - mpmri
  - brain
  - glioblastoma
  - glioma
  - brain-tumor
  - tumor-segmentation
  - brats
  - nifti
  - tcia
  - upenn-gbm
pretty_name: UPENN-GBM (mpMRI + Tumor Segmentation)
size_categories:
  - n<1K
dataset_info:
  features:
    - name: subject_id
      dtype: string
    - name: patient_id
      dtype: string
    - name: timepoint
      dtype: string
    - name: gt_source
      dtype: string
    - name: num_slices
      dtype: int32
    - name: slice_index
      dtype: int32
    - name: tumor_voxels
      dtype: int64
    - name: modality
      dtype: string
    - name: image
      dtype: image
    - name: mask
      dtype: image
    - name: overlay
      dtype: image
  splits:
    - name: manual
      num_bytes: 5989524
      num_examples: 147
    - name: automated
      num_bytes: 18932953
      num_examples: 464
    - name: unsegmented
      num_bytes: 856502
      num_examples: 60
  download_size: 25763705
  dataset_size: 25778979
configs:
  - config_name: default
    data_files:
      - split: manual
        path: data/manual-*
      - split: automated
        path: data/automated-*
      - split: unsegmented
        path: data/unsegmented-*

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

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}
}