---
dataset_info:
  features:
  - name: volume_id
    dtype: string
  - name: slice_id
    dtype: int32
  - name: t1
    dtype: image
  - name: t1c
    dtype: image
  - name: t2
    dtype: image
  - name: tumor_mask
    dtype: image
  - name: is_tumorous
    dtype: bool
  - name: tumor_type
    dtype: string
  - name: who_grade
    dtype: string
  - name: sex
    dtype: string
  - name: age
    dtype: float32
  splits:
  - name: train
    num_bytes: 1554208362.105
    num_examples: 77655
  download_size: 1591700680
  dataset_size: 1554208362.105
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- image-segmentation
- image-classification
language:
- en
tags:
- mri
- medical
- t1
- t2
- t1c
- medical volumes
- brain
- scans
- tumor
pretty_name: The University of California San Francisco Preoperative Diffuse Glioma MRI
size_categories:
- 10K<n<100K
---

# UCSF_PDGM — 2D Slice-Based Multi-Sequence Brain MRI Dataset with Tumor Segmentation

This dataset is derived from the **UCSF Preoperative Diffuse Glioma MRI (UCSF-PDGM)** collection, a large-scale, preoperative brain MRI dataset of patients with histopathologically confirmed diffuse gliomas, hosted on **The Cancer Imaging Archive (TCIA)**. The original collection provides skull-stripped, co-registered, multi-sequence 3D MRI volumes along with expert-corrected multicompartment tumor segmentations and detailed clinical/molecular metadata.

Here, we provide a **2D slice-based version**, extracted from three of the available MRI sequences per patient — **T1-weighted (T1)**, **post-contrast T1-weighted (T1c)**, and **T2-weighted (T2)** — paired slice-for-slice with the corresponding tumor segmentation mask and patient-level clinical metadata. The dataset is designed for multimodal tumor segmentation, classification, and representation learning on brain MRI.

---

## 📦 Dataset Structure

The dataset has a single `train` split, with each row corresponding to a single 2D axial slice from a patient's coregistered T1 / T1c / T2 volumes:

| Field | Description |
|---|---|
| `volume_id` | Unique patient/case identifier (e.g., `UCSF-PDGM-0152`) |
| `slice_id` | Index of the axial slice within the volume |
| `t1` | 2D pre-contrast T1-weighted MRI slice |
| `t1c` | 2D post-contrast (gadolinium-enhanced) T1-weighted MRI slice |
| `t2` | 2D T2-weighted MRI slice |
| `tumor_mask` | 2D multicompartment tumor segmentation mask (see **Tumor Mask Labels** below) |
| `is_tumorous` | Boolean — whether any tumor label is present on this slice |
| `tumor_type` | Final pathologic diagnosis (WHO 2021 classification) |
| `who_grade` | WHO CNS tumor grade: `2`, `3`, or `4` |
| `sex` | Patient sex: `M` or `F` |
| `age` | Patient age at MRI, in years |

`tumor_type`, `who_grade`, `sex`, and `age` are patient-level attributes and are therefore identical across all slices belonging to the same `volume_id`.

`tumor_type` takes one of four values, per the integrated WHO CNS 2021 diagnostic categories used by UCSF-PDGM:
- Glioblastoma, IDH-wildtype
- Astrocytoma, IDH-mutant
- Astrocytoma, IDH-wildtype
- Oligodendroglioma, IDH-mutant, 1p/19q-codeleted

---

## ⚙️ Preprocessing

- For each patient, the preprocessed, skull-stripped, and co-registered **T1**, **T1c**, and **T2** NIfTI volumes were used (all UCSF-PDGM volumes are resampled to a shared 1mm isotropic space defined by the T2/FLAIR image)
- Volumes were sliced into 2D axial slices; the tumor segmentation volume was sliced identically and aligned per-slice with its source volume
- Patient-level clinical metadata (`tumor_type`, `who_grade`, `sex`, `age`) was broadcast from the UCSF-PDGM clinical data table to every slice belonging to that patient
- All slices (tumorous and non-tumorous) were exported; use `is_tumorous` to filter or balance

---

## 🚀 Usage

```python
from datasets import load_dataset
import matplotlib.pyplot as plt
import numpy as np

ds = load_dataset("chehablab/UCSF_PDGM", split="train")

# Grab a tumorous slice and view all three sequences plus the mask overlay
sample = next(s for s in ds if s["is_tumorous"])

fig, axes = plt.subplots(1, 4, figsize=(16, 4))
for ax, key in zip(axes[:3], ["t1", "t1c", "t2"]):
    ax.imshow(sample[key], cmap="gray")
    ax.set_title(key.upper())
    ax.axis("off")

axes[3].imshow(sample["t1c"], cmap="gray")
axes[3].imshow(np.array(sample["tumor_mask"]), cmap="jet", alpha=0.4, vmin=0, vmax=4)
axes[3].set_title("Tumor Mask Overlay")
axes[3].axis("off")

fig.suptitle(
    f"{sample['volume_id']} | Slice {sample['slice_id']} | "
    f"{sample['tumor_type']} (WHO grade {sample['who_grade']})"
)
plt.tight_layout()
plt.show()
```

---

## 🏷️ Tumor Mask Labels

Tumor segmentation in UCSF-PDGM was generated as part of the **2021 BraTS challenge** pipeline (automated ensemble segmentation, manually corrected by trained radiologists and approved by expert reviewers), and follows the standard BraTS multicompartment labeling convention:

| Value | Label | Description |
|---|---|---|
| 0 | Background | No tumor |
| 1 | NCR/NET | Necrotic and non-enhancing tumor core |
| 2 | ED | Peritumoral edema / surrounding FLAIR abnormality |
| 4 | ET | GD-enhancing tumor |

Note label `3` is unused (a holdover from the original BraTS labeling scheme). Common derived regions of interest: **Whole Tumor** = {1, 2, 4}, **Tumor Core** = {1, 4}, **Enhancing Tumor** = {4}.

---

## 🧪 Use Cases

- Multimodal (T1 / T1c / T2) brain tumor segmentation
- WHO grade or tumor subtype classification from imaging
- Multi-task learning combining segmentation with clinical metadata prediction
- Slice-level pretraining and representation learning for glioma MRI
- Studying enhancing vs. non-enhancing tumor compartments across sequences

---

## ⚠️ Important Notes

- **Class imbalance**: most slices, particularly toward the superior/inferior extremes of a volume, contain no tumor. Use `is_tumorous` to filter or apply weighted sampling.
- **Volume-level splits**: always split train/val/test at the `volume_id` level, never at the slice level, to avoid leakage between sets.
- **Patient duplicates**: per TCIA's documentation, a small number of UCSF-PDGM case IDs are short-interval follow-up imaging of another case in the collection rather than fully independent patients. Be mindful of this when constructing splits across the full dataset.
- **Mask-image alignment**: `tumor_mask` is in the same 2D slice space as `t1`, `t1c`, and `t2` for a given row, so no additional registration is needed to overlay it.

---

## 📚 Citation

This dataset is derived from data made available on **The Cancer Imaging Archive (TCIA)** under a **CC BY 4.0** license. If you use this dataset, please acknowledge **[Chehab Lab](https://chehablab.com/)** and cite the original UCSF-PDGM dataset:

```bibtex
@misc{Calabrese2022,
  author = {Calabrese, E. and Villanueva-Meyer, J. and Rudie, J. and Rauschecker, A. and Baid, U. and Bakas, S. and Cha, S. and Mongan, J. and Hess, C.},
  title  = {The University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) (Version 5) [dataset]},
  year   = {2022},
  publisher = {The Cancer Imaging Archive},
  doi    = {10.7937/tcia.bdgf-8v37}
}
```

---

## 📜 License

This dataset is released under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license, consistent with the source data on TCIA.
You may copy, modify, distribute, and use the data, even for commercial purposes, **provided that appropriate credit is given** to the original authors and TCIA.

[![CC BY 4.0](https://licensebuttons.net/l/by/4.0/88x31.png)](https://creativecommons.org/licenses/by/4.0/)

**Chehab Lab @ 2026**