利用多模态 MRI 图像进行准确的脑肿瘤分割至关重要，但临床实践中缺少模态往往会降低准确性。本研究的目的是提出一个专家和语义引导网络的混合体，以解决脑肿瘤分割中缺失模式的问题。我们引入了一种基于变压器的编码器，具有新颖的专家混合块。在每个模块中，四名模态专家致力于特定模态的特征学习。采用可学习的模态嵌入来减轻缺失模态的负面影响。我们还引入了一种由语义信息引导的解码器，旨在更加关注各种肿瘤区域。最后，我们与其他模型进行了广泛的比较实验以及消融实验，以验证所提出的模型在 BraTS2018 数据集上的性能。即使缺少模式，所提出的模型也可以准确地分割脑肿瘤子区域。在 15 种模态组合中，整个肿瘤的平均 Dice 得分为 0.81，肿瘤核心的平均 Dice 得分为 0.66，增强型肿瘤的平均 Dice 得分为 0.52，在大多数情况下实现了顶级或接近顶级的结果，同时还表现出较低的计算成本。我们的专家混合和语义引导网络在缺少模式的情况下实现了准确可靠的脑肿瘤分割结果，表明其临床应用的巨大潜力。我们的源代码已在 https://github.com/MaggieLSY/MESG-Net 上提供。© 2024。国际医学和生物工程联合会。

Accurate brain tumor segmentation with multi-modal MRI images is crucial, but missing modalities in clinical practice often reduce accuracy. The aim of this study is to propose a mixture-of-experts and semantic-guided network to tackle the issue of missing modalities in brain tumor segmentation. We introduce a transformer-based encoder with novel mixture-of-experts blocks. In each block, four modality experts aim for modality-specific feature learning. Learnable modality embeddings are employed to alleviate the negative effect of missing modalities. We also introduce a decoder guided by semantic information, designed to pay higher attention to various tumor regions. Finally, we conduct extensive comparison experiments with other models as well as ablation experiments to validate the performance of the proposed model on the BraTS2018 dataset. The proposed model can accurately segment brain tumor sub-regions even with missing modalities. It achieves an average Dice score of 0.81 for the whole tumor, 0.66 for the tumor core, and 0.52 for the enhanced tumor across the 15 modality combinations, achieving top or near-top results in most cases, while also exhibiting a lower computational cost. Our mixture-of-experts and sematic-guided network achieves accurate and reliable brain tumor segmentation results with missing modalities, indicating its significant potential for clinical applications. Our source code is already available at https://github.com/MaggieLSY/MESG-Net .© 2024. International Federation for Medical and Biological Engineering.