用于多序列MRI的混合融合Transformer
Hybrid-Fusion Transformer for Multisequence MRI
AI总结：
针对多序列MRI分割中模态特征未充分融合的问题，提出HFTrans，通过Transformer整合各模态及早期融合特征，并在两个公开数据集上超越现有最先进方法。
摘要：
随着全卷积网络（FCN）的出现，医学分割取得了指数级增长，而如今Transformer的成功又使我们来到一个转折点。然而，不同模态的特征尚未被充分整合到用于医学分割的Transformer中。在本工作中，我们提出了用于多序列MRI图像分割的新型混合融合Transformer（HFTrans）。我们利用多模态MRI序列之间的差异，并使用Transformer层整合从每种模态提取的特征以及早期融合模态的特征。我们在三维（3D）医学分割中验证了这种混合融合方法的有效性。在两个公开数据集BraTS2020和MRBrainS18上的实验表明，所提出方法在脑肿瘤分割和脑结构分割任务上优于以往的最先进方法。
英文摘要：
Medical segmentation has grown exponentially through the advent of a fully convolutional network (FCN), and we have now reached a turning point through the success of Transformer. However, the different characteristics of the modality have not been fully integrated into Transformer for medical segmentation. In this work, we propose the novel hybrid fusion Transformer (HFTrans) for multisequence MRI image segmentation. We take advantage of the differences among multimodal MRI sequences and utilize the Transformer layers to integrate the features extracted from each modality as well as the features of the early fused modalities. We validate the effectiveness of our hybrid-fusion method in three-dimensional (3D) medical segmentation. Experiments on two public datasets, BraTS2020 and MRBrainS18, show that the proposed method outperforms previous state-of-the-art methods on the task of brain tumor segmentation and brain structure segmentation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
