多模型数据可以增强脑肿瘤分割的效果，因为它提供了丰富的信息。然而，它也引入了一些冗余信息，干扰了分割估计，因为某些模态可能会捕捉到与感兴趣的组织无关的特征。此外，不同等级肿瘤的模糊边界和不规则形状导致了对分割质量的非准确估计。鉴于这些问题，我们利用一个多头的不确定性引导的U形转换器来构建适用于稳健训练的dropout格式掩码。具体而言，我们的dropout掩码由边界掩码、先验概率掩码和条件概率掩码组成，可以帮助我们的方法更加关注不确定性区域。广泛的实验结果表明，我们的方法在BraTS2021数据集上实现了与先前最先进的脑肿瘤分割方法相当或更高的结果，达到了平均Dice系数为[Formula: see text]和Hausdorff距离为4.91。我们的代码可以在https://github.com/chaineypung/BTS-UGT上免费获取。© 2023. 国际医学与生物工程联合会。

Multi-model data can enhance brain tumor segmentation for the rich information it provides. However, it also introduces some redundant information that interferes with the segmentation estimation, as some modalities may catch features irrelevant to the tissue of interest. Besides, the ambiguous boundaries and irregulate shapes of different grade tumors lead to a non-confidence estimate of segmentation quality. Given these concerns, we exploit an uncertainty-guided U-shaped transformer with multiple heads to construct drop-out format masks for robust training. Specifically, our drop-out masks are composed of boundary mask, prior probability mask, and conditional probability mask, which can help our approach focus more on uncertainty regions. Extensive experimental results show that our method achieves comparable or higher results than previous state-of-the-art brain tumor segmentation methods, achieving average dice coefficients of [Formula: see text] and Hausdorff distance of 4.91 on the BraTS2021 dataset. Our code is freely available at https://github.com/chaineypung/BTS-UGT.© 2023. International Federation for Medical and Biological Engineering.