[Multimodal high-grade glioma semantic segmentation network with multi-scale and multi-attention fusion mechanism]

Glioma is a primary brain tumor with high incidence rate. High-grade gliomas (HGG) are those with the highest degree of malignancy and the lowest degree of survival. Surgical resection and postoperative adjuvant chemoradiotherapy are often used in clinical treatment, so accurate segmentation of tumor-related areas is of great significance for the treatment of patients. In order to improve the segmentation accuracy of HGG, this paper proposes a multi-modal glioma semantic segmentation network with multi-scale feature extraction and multi-attention fusion mechanism. The main contributions are, (1) Multi-scale residual structures were used to extract features from multi-modal gliomas magnetic resonance imaging (MRI); (2) Two types of attention modules were used for features aggregating in channel and spatial; (3) In order to improve the segmentation performance of the whole network, the branch classifier was constructed using ensemble learning strategy to adjust and correct the classification results of the backbone classifier. The experimental results showed that the Dice coefficient values of the proposed segmentation method in this article were 0.909 7, 0.877 3 and 0.839 6 for whole tumor, tumor core and enhanced tumor respectively, and the segmentation results had good boundary continuity in the three-dimensional direction. Therefore, the proposed semantic segmentation network has good segmentation performance for high-grade gliomas lesions.脑胶质瘤是一种发病率较高的原发性脑部肿瘤，其中高等级胶质瘤恶性程度高，患者生存率低。临床常采用手术切除和术后辅助放化疗的方式进行治疗，因此准确分割肿瘤相关区域对患者的治疗具有重要意义。为改善高等级胶质瘤的分割精度，本文提出一种基于多尺度特征提取、多路注意力融合机制的多模态脑胶质瘤分割方法，主要贡献在于：① 使用多尺度残差结构对多模态脑胶质瘤磁共振图像进行特征提取；② 使用两类注意力模块结构对通道维度和空间维度下的特征信息进行注意力汇聚；③ 使用集成学习策略构建支路分类器对主干分类器的分类结果进行调整修正，提升整体网络的分割性能。实验结果表明本文提出的二维网络分割方法分割全肿瘤区、肿瘤核心区和增强肿瘤区三类目标物的Dice系数值分别为0.909 7、0.877 3和0.839 6，并且分割结果在三维方向上具有良好的边界连续性。因此，本文提出的语义分割网络对高等级脑胶质瘤病灶区具有良好的分割性能。.