Magnetic resonance imaging (MRI) is critically important for brain mapping in both scientific research and clinical studies. Precise segmentation of brain tumors facilitates clinical diagnosis, evaluations, and surgical planning. Deep learning has recently emerged to improve brain tumor segmentation and achieved impressive results. Convolutional architectures are widely used to implement those neural networks. By the nature of limited receptive fields, however, those architectures are subject to representing long-range spatial dependencies of the voxel intensities in MRI images. Transformers have been leveraged recently to address the above limitations of convolutional networks. Unfortunately, the majority of current Transformers-based methods in segmentation are performed with 2D MRI slices, instead of 3D volumes. Moreover, it is difficult to incorporate the structures between layers because each head is calculated independently in the Multi-Head Self-Attention mechanism (MHSA). In this work, we proposed a 3D Transformer-based segmentation approach. We developed a Fusion-Head Self-Attention mechanism (FHSA) to combine each attention head through attention logic and weight mapping, for the exploration of the long-range spatial dependencies in 3D MRI images. We implemented a plug-and-play self-attention module, named the Infinite Deformable Fusion Transformer Module (IDFTM), to extract features on any deformable feature maps. We applied our approach to the task of brain tumor segmentation, and assessed it on the public BRATS datasets. The experimental results demonstrated that our proposed approach achieved superior performance, in comparison to several state-of-the-art segmentation methods.

翻译：磁共振成像（MRI）在科学研究和临床研究的脑图谱绘制中至关重要。脑肿瘤的精确分割有助于临床诊断、评估和手术规划。近年来，深度学习在脑肿瘤分割领域取得显著进展，卷积架构被广泛用于实现这些神经网络。然而，受限于感受野的固有特性，此类架构难以表达MRI图像中体素强度的长程空间依赖关系。Transformer最近被用于解决卷积网络的上述局限，但现有基于Transformer的分割方法大多在二维MRI切片上操作，而非三维体数据。此外，多头自注意力机制（MHSA）中每个注意力头独立计算，导致层间结构整合困难。本研究提出一种基于三维Transformer的分割方法，通过设计融合头自注意力机制（FHSA），利用注意力逻辑与权重映射合并各注意力头，以探索三维MRI图像中的长程空间依赖关系。我们实现了一个即插即用的自注意力模块——无限可变形融合Transformer模块（IDFTM），用于在任意可变形特征图上提取特征。将该方法应用于脑肿瘤分割任务，并在公开BRATS数据集上评估。实验结果表明，与多种先进分割方法相比，本方法取得了更优的性能。