发表机构
Pontifícia Universidade Católica do Paraná (PUCPR); University of Luxembourg（巴拉那天主教大学; 卢森堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对BraTS 2026挑战赛的跨肿瘤泛化脑肿瘤分割任务，采用nnU-Net框架结合自训练与肿瘤感知形变增强，在验证集上取得优于基线的分割性能，源代码公开可用。
AI 中文摘要
本研究针对BraTS 2026挑战赛的跨肿瘤泛化（BraTS-GoAT）任务提出了一种方法，该任务聚焦于在异质性患者群体中对脑肿瘤亚区进行鲁棒分割。所提方法采用nnU-Net框架与大型残差编码器架构，将半监督学习技术与从未标记训练数据生成的伪标签、以及能在保留周围解剖结构的同时对病灶进行局部形变的肿瘤感知形变增强相结合。我们采用不同比例的最具置信度伪标记样本，评估各组件及其组合的单独贡献。针对泛化任务提交的配置，在BraTS-GoAT验证集上，全肿瘤的Dice和NSD分数分别为0.881和0.473，肿瘤核心分别为0.817和0.490，增强肿瘤分别为0.775和0.533，在所有肿瘤区域均优于仅使用标记数据的基线，证实自训练与所提增强具有互补性。我们的源代码公开于此https URL。
英文摘要
This work presents an approach to the Generalizability Across Tumors (BraTS-GoAT) task of the BraTS 2026 Challenge, which focuses on robust segmentation of brain tumor sub-regions across a heterogeneous patient population. The proposed method employs the nnU-Net framework with a large residual encoder architecture, integrating a semi-supervised learning technique with pseudo-labels generated from the unlabeled training data and a tumor-aware deformable augmentation that locally deforms the lesion while preserving the surrounding anatomy. We evaluate the individual contributions of each component, as well as their combination, using varying proportions of the most confident pseudo-labeled cases. The submitted configuration for the generalization task achieves Dice and NSD scores of 0.881 and 0.473 for Whole Tumor, 0.817 and 0.490 for Tumor Core, and 0.775 and 0.533 for Enhancing Tumor on the BraTS-GoAT validation set, improving over the labeled-only baselines across all tumor regions and confirming that self-training and the proposed augmentation are complementary. Our source code is publicly available at https://github.com/Henrique-zan/brats-goat-2026/.
CommentsAccepted for presentation at the 2026 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) - BraTS Cluster of Challenges
