Accurate segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) remain key challenges in medical image analysis, primarily due to the lack of high-quality, balanced, and diverse datasets with expert annotations. In this work, we address this gap by introducing BRISC, a dataset designed for brain tumor segmentation and classification tasks, featuring high-resolution segmentation masks. The dataset comprises 6,000 contrast-enhanced T1-weighted MRI scans, which were collated from multiple public datasets that lacked segmentation labels. Our primary contribution is the subsequent expert annotation of these images, performed by certified radiologists and physicians. It includes three major tumor types, namely glioma, meningioma, and pituitary, as well as non-tumorous cases. Each sample includes high-resolution labels and is categorized across axial, sagittal, and coronal imaging planes to facilitate robust model development and cross-view generalization. To demonstrate the utility of the dataset, we provide benchmark results for both tasks using standard deep learning models. The BRISC dataset is made publicly available. datasetlink: Kaggle (https://www.kaggle.com/datasets/briscdataset/brisc2025/), Figshare (https://doi.org/10.6084/m9.figshare.30533120), Zenodo (https://doi.org/10.5281/zenodo.17524350)

翻译：基于磁共振成像（MRI）的脑肿瘤精确分割与分类仍是医学图像分析中的关键挑战，这主要源于缺乏高质量、平衡且多样化的专家标注数据集。本研究通过引入BRISC数据集来填补这一空白，该数据集专为脑肿瘤分割与分类任务设计，具备高分辨率分割掩码。数据集包含6,000例对比增强T1加权MRI扫描，这些数据整合自多个缺乏分割标签的公开数据集。我们的核心贡献在于随后由认证放射科医师和医生对这些图像进行的专家标注。标注涵盖三种主要肿瘤类型，即胶质瘤、脑膜瘤和垂体瘤，以及非肿瘤病例。每个样本均包含高分辨率标签，并按轴向、矢状面和冠状面成像平面分类，以促进鲁棒的模型开发和跨视图泛化。为展示数据集的实用性，我们使用标准深度学习模型为两项任务提供了基准结果。BRISC数据集已公开提供。数据集链接：Kaggle（https://www.kaggle.com/datasets/briscdataset/brisc2025/）、Figshare（https://doi.org/10.6084/m9.figshare.30533120）、Zenodo（https://doi.org/10.5281/zenodo.17524350）