| --- |
| 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. |
|
|
| [](https://creativecommons.org/licenses/by/4.0/) |
|
|
| **Chehab Lab @ 2026** |
