BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification

BRISC 2025 — Brain Tumor MRI DatasetBRISC (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🚀 OverviewBRISC 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 structurebrisc2025/├─ 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.mdNotes:- 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📄 CitationIf 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}}🤝 AcknowledgmentsThanks to the collaborating radiologists and physicians for expert annotation and review.🔗 References & inspirationsThis dataset drew design and organizational inspiration from widely used brain tumor imaging datasets (e.g., BraTS, Figshare datasets, Kaggle collections). See the project paper for full details and evaluation results.