🧠 BRISC 2025

BRISC 2025 — Brain Tumor MRI Dataset

BRISC (BRain tumor Image Segmentation & Classification) — a curated, expert-annotated T1 MRI dataset for multi-class brain tumor classification and pixel-wise segmentation.

Published in: Scientific Data (Nature Portfolio)
DOI: https://doi.org/10.1038/s41597-026-06753-y

🚀 Overview
BRISC is designed to address common shortcomings in existing public brain MRI collections (e.g., class imbalance, limited tumor types, and annotation inconsistency). It provides high-quality, physician-validated pixel-level masks and a balanced multi-class classification split, suitable for benchmarking segmentation and classification algorithms as well as multi-task learning research.

Highlights
- 6,000 T1-weighted MRI slices (5,000 train / 1,000 test)
- Four classes: Glioma, Meningioma, Pituitary Tumor, No Tumor
- Pixel-wise segmentation masks reviewed by radiologists
- Slices from three anatomical planes: Axial, Coronal, Sagittal
- Clean, stratified train/test splits and aligned image–mask filenames

📦 Dataset structure
brisc2025/
├─ classification_task/
│ ├─ train/
│ │ ├─ glioma/
│ │ │ ├─ brisc2025_train_00001_gl_ax_t1.jpg
│ │ │ └─ ...
│ │ ├─ meningioma/
│ │ ├─ pituitary/
│ │ └─ no_tumor/
│ └─ test/
│ ├─ glioma/
│ │ ├─ brisc2025_test_00001_gl_ax_t1.jpg
│ │ └─ ...
│ ├─ meningioma/
│ ├─ pituitary/
│ └─ no_tumor/
├─ segmentation_task/
│ ├─ train/
│ │ ├─ images/
│ │ │ ├─ brisc2025_train_00001_gl_ax_t1.jpg
│ │ │ └─ ...
│ │ └─ masks/
│ │ ├─ brisc2025_train_00001_gl_ax_t1.png
│ │ └─ ...
│ └─ test/
│ ├─ images/
│ │ ├─ brisc2025_test_00001_gl_ax_t1.jpg
│ │ └─ ...
│ └─ masks/
│ ├─ brisc2025_test_00001_gl_ax_t1.png
│ └─ ...
├─ manifest.json
├─ manifest.csv
├─ manifest.json.sha256
├─ manifest.csv.sha256
└─ README.md

Notes:
- Classification folders contain image-level labels suitable for standard image classification pipelines.
- Segmentation folders contain paired MRI images/ and corresponding binary masks/.
- Image and mask filenames are identical except for file extension (images: .jpg, masks: .png).
- All images are T1-weighted slices.

📊 Dataset statistics

- Total samples: 6,000 (5,000 train / 1,000 test)
- Classes: 4 (balanced distribution across train/test)
- Planes: Axial / Coronal / Sagittal (balanced representation)
- Imaging modality: T1-weighted MRI
- Annotation quality: Reviewed and corrected by medical experts

📄 Citation
If you use BRISC in your work, please cite:

@article{fateh2025brisc,
title={Brisc: Annotated dataset for brain tumor segmentation and classification with swin-hafnet},
author={Fateh, Amirreza and Rezvani, Yasin and Moayedi, Sara and Rezvani, Sadjad and Fateh, Fatemeh and Fateh, Mansoor and Abolghasemi, Vahid},
journal={arXiv preprint arXiv:2506.14318},
year={2025}
}

🔗 References & inspirations
This dataset drew design and organizational inspiration from widely used brain tumor imaging datasets (e.g., BraTS, Figshare datasets, Kaggle collections).