GLI-AL：一个具有统一解剖-病灶标签的多模态胶质瘤MRI标签资源

摘要

现有BraTS-GLI数据集为成人胶质瘤MRI分割提供了广泛使用的基准，但其任务定义聚焦于肿瘤亚区，未能系统表征共存的脑白质高信号（WMH）。在联合分割场景中，此类未标注的异常区域会因将病理区域视为正常组织而引入特定任务的标签噪声。为弥补这一不足，我们提出BraTS-GLI-解剖-病灶数据集，这是一个基于BraTS 2023-GLI训练队列构建的受控访问、仅含标签的衍生资源。该资源提供1,251套与原始四模态MRI病例对齐的八类统一解剖-病灶标签集，其中包含116例需要修复图像输入病例的图像修复标签。队列被组织为394例纯化子集与857例扩展子集，附带涵盖标签来源、图像修复需求、质量控制状态、访问条件、校验和及发布边界的病例级元数据。相较于原始BraTS-GLI标注，该资源通过在统一标签空间中纳入健康脑组织及此前未标注的共存异常区域，显著扩展了前景监督范围。使用MedNeXt与T1/FLAIR输入的验证研究表明，基于WMH感知的监督能在领域内GLI数据集与外部WMH数据集上保持健康组织分割性能，同时相较于含噪对照训练，提升了对共存病灶的敏感性。该资源面向科学研究，支持联合解剖-病灶监督、标签噪声分析及可重复评估。数据获取地址：https://www.synapse.org/Synapse:syn75210889/wiki/，代码获取地址：https://github.com/xyx200/brats-gli-anatomy-lesion-code。数据资源DOI：https://doi.org/10.7303/SYN75210889。

English

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.