Abstract: Glioma is a primary tumor originating from glial cells in the brain,accounting for about 45% of all intracranial tumors.Accurate segmentation of brain tumor in magnetic resonance imaging(MRI) images is of great clinical significance.In this paper,an automatic brain tumor segmentation method based on multi-encoder architecture is proposed.The model adopts a U-shaped network structure which expands the single contracting path into multiple paths to deeply exploit semantic information of diffe-rent modalities.In order to obtain the multiscale features of images,an inception module combined with dilated convolution is designed as the basic convolutional layer;a lightweight attention mechanism known as efficient channel attention(ECA) block is then introduced into the bottleneck layer and the decoder,so that the model pays more attention to the segmentation-related information and ignores the redundancy of the channel dimension,thereby further improving the segmentation results.Using the Brain Tumor Segmentation Challenge 2018(BraTS 2018) dataset for verification,the proposed model gets average Dice coefficientvalues of 0.880,0.784,and 0.757 for the whole tumor,tumor core and enhancing tumor respectively.Experiment results show that the proposed method achieves accurate and effective multimodal MRI brain tumor segmentation.

Key words: Image processing, Brain tumor segmentation, Muti-encoder, Dilated convolution, Channel attention mechanism