  Automatic segmentation of medical images based on multi-modality is an
important topic for disease diagnosis. Although the convolutional neural
network (CNN) has been proven to have excellent performance in image
segmentation tasks, it is difficult to obtain global information. The lack of
global information will seriously affect the accuracy of the segmentation
results of the lesion area. In addition, there are visual representation
differences between multimodal data of the same patient. These differences will
affect the results of the automatic segmentation methods. To solve these
problems, we propose a segmentation method suitable for multimodal medical
images that can capture global information, named TranSiam. TranSiam is a 2D
dual path network that extracts features of different modalities. In each path,
we utilize convolution to extract detailed information in low level stage, and
design a ICMT block to extract global information in high level stage. ICMT
block embeds convolution in the transformer, which can extract global
information while retaining spatial and detailed information. Furthermore, we
design a novel fusion mechanism based on cross attention and selfattention,
called TMM block, which can effectively fuse features between different
modalities. On the BraTS 2019 and BraTS 2020 multimodal datasets, we have a
significant improvement in accuracy over other popular methods.
