GLI-AL：一个具有统一解剖-病变标签的多模态胶质瘤MRI标签资源
GLI-AL: A Multi-Modal Glioma MRI Label Resource with Unified Anatomy-Lesion Labels
浏览论文内容
中文总结 AI 辅助
研究针对现有BraTS-GLI数据集不足，引入BraTS-GLI Anatomy-Lesion资源，提供统一解剖-病变标签集及元数据。通过WMH感知监督，提升对共存病变敏感性，保持健康组织分割性能，支持多方面研究，数据和代码可获取。
中文摘要 AI 辅助
现有的BraTS-GLI数据集为成人胶质瘤MRI分割提供了广泛使用的基准，但它们的任务定义侧重于肿瘤子区域，没有系统地表示共存的白质高信号（WMH）。在联合分割设置中，这种未标记的异常将病理区域视为正常组织，从而引入特定任务的标签噪声。为了解决这一限制，我们引入了BraTS-GLI Anatomy-Lesion，这是一个从BraTS 2023-GLI训练队列构建的访问受限、仅包含标签的派生资源。该资源提供了1251个统一的八类解剖-病变标签集，与原始的四模态MRI病例对齐，包括116例需要修复成像输入的图像修复标签。该队列分为一个394例的纯化子集和一个857例的扩展子集，病例级元数据涵盖标签来源、图像修复要求、质量控制状态、访问条件、校验和和发布边界。与原始的BraTS-GLI注释相比，该资源通过在统一标签空间中纳入健康脑组织和以前未标记的共存异常，大幅扩展了前景监督。使用MedNeXt和T1/FLAIR输入的验证研究表明，WMH感知监督在域内GLI和外部WMH数据集中都能保持健康组织分割性能，同时相对于噪声控制训练提高了对共存病变的敏感性。该资源旨在用于科学研究，支持联合解剖-病变监督、标签噪声分析和可重复评估。数据可在此https URL获取，代码可在此https URL获取。数据资源DOI为此https URL。
英文摘要
Existing BraTS-GLI datasets provide a widely used benchmark for adult glioma MRI segmentation, but their task definition focuses on tumor subregions and does not systematically represent coexisting white matter hyperintensities (WMH). In joint segmentation settings, such unlabeled abnormalities introduce task-specific label noise by treating pathological regions as normal tissue. To address this limitation, we introduce BraTS-GLI Anatomy-Lesion, a controlled-access, labels-only derived resource built from the BraTS 2023-GLI training cohort. The resource provides 1,251 unified eight-class anatomy-lesion label sets aligned with the original four-modal MRI cases, including image-repair labels for 116 cases requiring repaired imaging inputs. The cohort is organized into a 394-case purified subset and an 857-case extended subset, with case-level metadata covering label source, image-repair requirements, quality-control status, access conditions, checksums, and release boundaries. Compared with the original BraTS-GLI annotations, the resource substantially expands foreground supervision by incorporating healthy brain tissues and previously unlabeled coexisting abnormalities within a unified label space. A validation study using MedNeXt and T1/FLAIR inputs suggests that WMH-aware supervision preserves healthy-tissue segmentation performance across both in-domain GLI and external WMH datasets, while improving sensitivity to coexisting lesions relative to noisy-control training. The resource is intended for scientific research and supports joint anatomy-lesion supervision, label-noise analysis, and reproducible evaluation. Data are available at https://www.synapse.org/Synapse:syn75210889/wiki/, and code is available at https://github.com/xyx200/brats-gli-anatomy-lesion-code. The data resource DOI is https://doi.org/10.7303/SYN75210889.
发表机构
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University（教育部生物医学信息工程重点实验室，西安交通大学生命科学与技术学院）
- School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学院）
- Research Center for Intelligent Medical Equipment and Devices (IMED), Xi’an Jiaotong University（西安交通大学智能医疗设备与器械研究中心）
机构由 AI 辅助整理，请以论文原文为准。
