  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.
