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: https://www.kaggle.com/datasets/briscdataset/brisc2025/

翻译：从磁共振成像（MRI）中准确分割和分类脑肿瘤仍然是医学图像分析中的关键挑战，这主要是由于缺乏具有专家标注的高质量、平衡且多样化的数据集。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，并包含高分辨率分割掩码。该数据集包含6000个对比增强T1加权MRI扫描，这些扫描是从多个缺乏分割标签的公共数据集中整理而来。我们的主要贡献在于随后对这些图像进行的专家标注，由认证的放射科医生和医师完成。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进行分类，以促进稳健的模型开发和跨视图泛化。为展示该数据集的实用性，我们使用标准深度学习模型为两项任务提供了基准测试结果。BRISC数据集已公开提供。数据集链接：https://www.kaggle.com/datasets/briscdataset/brisc2025/