🧠 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).