Datasets:
File size: 5,766 Bytes
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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
- 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
```bibtex
@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}
}
```
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