计算机与数字工程2025，Vol.53Issue(3)：648-651,724,5.DOI:10.3969/j.issn.1672-9722.2025.03.007
基于轴向反向注意力机制的BS-Net脑瘤分割算法
BS-Net Brain Tumor Segmentation Algorithm Based on Axial Reverse Attention Mechanism
唐明 1张文静 2白俊卿 2吴晨俣 2徐东2
作者信息
- 1. 西安培华学院 西安 710125
- 2. 西安石油大学 西安 710065
- 折叠
摘要
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.关键词
轴向反向注意力机制/Res2Net/特征金字塔/脑瘤图像分割Key words
axial reverse attention mechanism/Res2Net/featured pyramid networks/brain tumor image segmentation分类
信息技术与安全科学引用本文复制引用
唐明,张文静,白俊卿,吴晨俣,徐东..基于轴向反向注意力机制的BS-Net脑瘤分割算法[J].计算机与数字工程,2025,53(3):648-651,724,5.
