  Glioma is the most common and aggressive brain tumor. Magnetic resonance
imaging (MRI) plays a vital role to evaluate tumors for the arrangement of
tumor surgery and the treatment of subsequent procedures. However, the manual
segmentation of the MRI image is strenuous, which limits its clinical
application. With the development of deep learning, a large number of automatic
segmentation methods have been developed, but most of them stay in 2D images,
which leads to subpar performance. Moreover, the serious voxel imbalance
between the brain tumor and the background as well as the different sizes and
locations of the brain tumor makes the segmentation of 3D images a challenging
problem. Aiming at segmenting 3D MRI, we propose a model for brain tumor
segmentation with multiple encoders. The structure contains four encoders and
one decoder. The four encoders correspond to the four modalities of the MRI
image, perform one-to-one feature extraction, and then merge the feature maps
of the four modalities into the decoder. This method reduces the difficulty of
feature extraction and greatly improves model performance. We also introduced a
new loss function named "Categorical Dice", and set different weights for
different segmented regions at the same time, which solved the problem of voxel
imbalance. We evaluated our approach using the online BraTS 2020 Challenge
verification. Our proposed method can achieve promising results in the
validation set compared to the state-of-the-art approaches with Dice scores of
0.70249, 0.88267, and 0.73864 for the intact tumor, tumor core, and enhanced
tumor, respectively.

Image and Video Processing;Artificial Intelligence;Computer Vision and Pattern Recognition