通过针对性丢弃T2-FLAIR序列，提升脑胶质瘤分割在缺图情况下的鲁棒性。
Robust Glioblastoma Segmentation and Volumetry Without T2-FLAIR: External Validation of Targeted Dropout Training
- 训练时故意屏蔽T2-FLAIR通道，让模型学会依赖其他影像模态。
- 无T2-FLAIR时整体分割精度达93.4%（原仅81.0%），体积误差从-45.6mL降至0.83mL。
- 适合临床异构数据、缺少完整MRI的脑瘤自动分析场景。
目的：在外部独立队列中验证针对T2-FLAIR的定向丢失策略，实现无需T2-FLAIR的鲁棒脑胶质瘤分割与全瘤体积测量，同时在完整MRI协议下保持性能。方法：本回顾性多数据集研究中，基于BraTS 2021（n=848）训练3D nnU-Net模型，并在独立的宾夕法尼亚大学脑胶质瘤队列（n=403）上进行外部验证。模型训练时采用或不采用针对T2-FLAIR的定向丢弃（训练时置零T2-FLAIR通道）。测试分预设的含与不含T2-FLAIR两种场景；后者通过推理时置零该通道模拟。主要终点为患者级区域级总体Dice相似系数（DSC）。次要终点包括区域特异性DSC、95%分位数豪斯多夫距离及Bland-Altman全瘤体积偏差。结果：外部验证中，完整协议下性能保持：有丢弃组中位DSC为94.8%（四分位距[IQR] 90.0%-97.1%），无丢弃组为95.0%（IQR 90.3%-97.1%）。在无T2-FLAIR场景下，定向丢弃使总体中位DSC从81.0%（IQR 75.1%-86.4%）提升至93.4%（IQR 89.1%-96.2%）。全瘤DSC由60.4%升至92.6%，95%分位数豪斯多夫距离由17.24 mm降至2.45 mm，全瘤体积偏差由-45.6 mL降至0.83 mL。结论：在独立外部队列中，定向丢弃策略在完整协议下保持分割性能，并显著降低无T2-FLAIR时的全瘤分割误差与体积偏差。该结果支持定向序列丢弃作为回顾性及异构临床流程中自动化脑胶质瘤分析的实用鲁棒性策略。
原文摘要 · Abstract (English)
Objectives: To externally validate targeted T2 fluid-attenuated inversion recovery (T2-FLAIR) dropout for robust automated glioblastoma segmentation and whole-tumor volumetry without T2-FLAIR, while preserving performance when the full MRI protocol is available. Methods: In this retrospective multi-dataset study, 3D nnU-Net models were developed on BraTS 2021 (n=848) and externally validated on an independent University of Pennsylvania glioblastoma cohort (n=403). Models were trained with or without targeted T2-FLAIR dropout, zeroing the T2-FLAIR channel during training. Testing used prespecified T2-FLAIR-present and T2-FLAIR-absent scenarios; the absent scenario was simulated by zeroing the T2-FLAIR channel at inference. The primary endpoint was per-patient overall region-wise Dice similarity coefficient (DSC). Secondary endpoints were region-specific DSC, 95th percentile Hausdorff distance, and Bland-Altman whole-tumor volume bias. Results: In external validation, performance was preserved with the full MRI protocol: overall median DSC was 94.8% (interquartile range [IQR] 90.0%-97.1%) with dropout and 95.0% (IQR 90.3%-97.1%) without dropout. In the T2-FLAIR-absent scenario, targeted dropout improved overall median DSC from 81.0% (IQR 75.1%-86.4%) to 93.4% (IQR 89.1%-96.2%). Whole-tumor DSC improved from 60.4% to 92.6%, whole-tumor 95th percentile Hausdorff distance from 17.24 mm to 2.45 mm, and whole-tumor volume bias from -45.6 mL to 0.83 mL. Conclusions: In an independent external test cohort, targeted T2-FLAIR dropout preserved glioblastoma segmentation performance with the full MRI protocol and substantially reduced whole-tumor segmentation error and volumetric bias when T2-FLAIR was absent. These findings support targeted sequence dropout as a practical robustness strategy for automated glioblastoma analysis in retrospective and heterogeneous clinical workflows.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。