BRISC数据集提供6000张脑肿瘤MRI高清标注图，助力精准分割与分类。
BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification
- 整合多源MRI数据并由专家标注，覆盖三大肿瘤类型及正常病例
- 含6000张增强T1加权图像，每例配高分辨率分割标签
- 支持多视角训练，适合医学影像模型开发与评测
从磁共振成像（MRI）中准确分割和分类脑肿瘤仍是医学图像分析中的关键挑战，主要源于高质量、平衡且多样化的带专家标注数据集的缺乏。本文提出BRISC数据集，专为脑肿瘤分割与分类任务设计，包含高分辨率分割掩码。该数据集共收录6,000例对比增强T1加权MRI扫描，来源为多个未附分割标签的公开数据集。其主要贡献在于由认证放射科医生和医师完成的专家标注工作，涵盖胶质瘤、脑膜瘤、垂体瘤三类主要肿瘤类型以及非肿瘤病例。每张图像均配有高分辨率标签，并按轴向、矢状面和冠状面进行分类，以促进模型稳健性与跨视角泛化能力。为验证数据集价值，我们使用标准深度学习模型在两项任务上提供了基准结果。BRISC数据集已公开可获取。
原文摘要 · Abstract (English)
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/
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