BS-Net Brain Tumor Segmentation Algorithm Based on Axial Reverse Attention Mechanism
TANG Ming
ZHANG Wenjing
BAI Junqing
WU Chenyu
XU Dong
Abstract：Aiming at the problems of brain tumor and surrounding tissue in the MRI image is not obvious,and the proportion of lesions in the image is relatively low,resulting in missed detection of cerebellar tumors,this paper proposes a BS-Net brain tu-mor segmentation algorithm based on axial reverse attention mechanism.Firstly,Res2Net with multi-scale residual units is used to extract the global characteristics of the image,which strengthens the attention to the diversity of brain tumor sizes.At the same time,the feature maps of different receptor fields are fused through the feature pyramid to obtain rich semantic information of small target brain tumors.Secondly,the axial reverse attention module is used to obtain the characteristic information containing more spa-tial location and semantic information of the lesion,and refine the boundary of the brain tumor lesion area.Finally,the BS-Net net-work is trained on the BraTS 2018 dataset to obtain a brain tumor image segmentation model.The model proposed in this paper com-pares the objective evaluation index and the visual segmentation effect.Experiments show that the network has a better effect on the detection of small targets,and the shape edges of the brain tumor lesion segmentation are closer to biology,which is of great signifi-cance in the clinical application of brain tumors.
Keywords：axial reverse attention mechanismRes2Netfeatured pyramid networksbrain tumor image segmentation
Publication Date：2025-03-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 648-651,724 )