GLI-AL：一個具有統一解剖-病灶標籤的多模態膠質瘤MRI標籤資源

摘要

現有的BraTS-GLI數據集為成人膠質瘤MRI分割提供了廣泛使用的基準，但其任務定義專注於腫瘤子區域，並未系統性地表徵共存的白質高信號（WMH）。在聯合分割設定中，此類未標註的異常區域會將病理區域視為正常組織，從而引入特定任務的標籤噪聲。為解決此限制，我們推出BraTS-GLI解剖病變數據集（BraTS-GLI Anatomy-Lesion），這是一個基於BraTS 2023-GLI訓練隊列構建、受控訪問且僅含標籤的派生資源。該資源提供1,251套統一的八類解剖-病變標籤集，與原始的四模態MRI病例對齊，其中包括116個需要修復影像輸入病例的影像修復標籤。此隊列組織為394例的純化子集與857例的擴展子集，並包含病例層級元數據（涵蓋標籤來源、影像修復需求、質控狀態、訪問條件、校驗和及發布邊界）。與原始BraTS-GLI標註相比，此資源透過在統一標籤空間中納入健康腦組織與先前未標註的共存異常，大幅擴展了前景監督範圍。使用MedNeXt與T1/FLAIR輸入的驗證研究顯示，WMH感知監督在域內GLI與外部WMH數據集中均能維持健康組織分割效能，同時相較於噪聲對照訓練，能提升對共存病變的敏感性。此資源專為科學研究設計，支援聯合解剖-病變監督、標籤噪聲分析及可重現評估。數據可於https://www.synapse.org/Synapse:syn75210889/wiki/獲取，程式碼可於https://github.com/xyx200/brats-gli-anatomy-lesion-code獲取。數據資源DOI為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.