Li Junjie, Peng Hong, Li Bing, Liu Zhicai, Lugu Rikong, He Bingyan
School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China.
Glagow College, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China.
Int J Neural Syst. 2025 Apr 28:2550036. doi: 10.1142/S0129065725500364.
Due to the differences in size, shape, and location of brain tumors, brain tumor segmentation differs greatly from that of other organs. The purpose of brain tumor segmentation is to accurately locate and segment tumors from MRI images to assist doctors in diagnosis, treatment planning and surgical navigation. NSNP-like convolutional model is a new neural-like convolutional model inspired by nonlinear spiking mechanism of nonlinear spiking neural P (NSNP) systems. Therefore, this paper proposes a global-local feature fusion network based on NSNP-like convolutional model for MRI brain tumor segmentation. To this end, we have designed three characteristic modules that take full advantage of the NSNP-like convolution model: dilated SNP module (DSNP), multi-path dilated SNP pooling module (MDSP) and Poolformer module. The DSNP and MDSP modules are employed to construct the encoders. These modules help address the issue of feature loss and enable the fusion of more high-level features. On the other hand, the Poolformer module is used in the decoder. It processes features that contain global context information and facilitates the interaction between local and global features. In addition, channel spatial attention (CSA) module is designed at the skip connection between encoder and decoder to establish the long-range dependence between the same layers, thereby enhancing the relationship between channels and making the model have global modeling capabilities. In the experiments, our model achieves Dice coefficients of 85.71[Formula: see text], 92.32[Formula: see text], 87.75[Formula: see text] for ET, WT, and TC, respectively, on the N-BraTS2021 dataset. Moreover, our model achieves Dice coefficients of 83.91[Formula: see text], 91.96[Formula: see text], 90.14[Formula: see text] and 85.05[Formula: see text], 92.30[Formula: see text], 90.31[Formula: see text] on the BraTS2018 and BraTS2019 datasets respectively. Experimental results also indicate that our model not only achieves good brain tumor segmentation performance, but also has good generalization ability. The code is already available on GitHub: https://github.com/Li-JJ-1/NSNP-brain-tumor-segmentation.
由于脑肿瘤在大小、形状和位置上存在差异，脑肿瘤分割与其他器官的分割有很大不同。脑肿瘤分割的目的是从磁共振成像（MRI）图像中准确地定位和分割肿瘤，以协助医生进行诊断、治疗规划和手术导航。类NSNP卷积模型是一种受非线性脉冲神经P（NSNP）系统的非线性脉冲机制启发的新型类神经卷积模型。因此，本文提出了一种基于类NSNP卷积模型的全局-局部特征融合网络用于MRI脑肿瘤分割。为此，我们设计了三个充分利用类NSNP卷积模型的特征模块：扩张SNP模块（DSNP）、多路径扩张SNP池化模块（MDSP）和Poolformer模块。DSNP和MDSP模块用于构建编码器。这些模块有助于解决特征丢失问题，并能融合更多的高级特征。另一方面，Poolformer模块用于解码器。它处理包含全局上下文信息的特征，并促进局部和全局特征之间的交互。此外，在编码器和解码器之间的跳跃连接处设计了通道空间注意力（CSA）模块，以建立同一层之间的长程依赖关系，从而增强通道之间的关系，使模型具有全局建模能力。在实验中，我们的模型在N-BraTS2021数据集上，对于增强肿瘤（ET）、全肿瘤（WT）和肿瘤核心（TC）的Dice系数分别达到85.71[公式：见正文]、92.32[公式：见正文]、87.75[公式：见正文]。此外，我们的模型在BraTS2018和BraTS2019数据集上分别达到了83.91[公式：见正文]、91.96[公式：见正文]、90.14[公式：见正文]和85.05[公式：见正文]、92.30[公式：见正文]、90.31[公式：见正文]的Dice系数。实验结果还表明，我们的模型不仅实现了良好的脑肿瘤分割性能，而且具有良好的泛化能力。代码已在GitHub上提供：https://github.com/Li-JJ-1/NSNP-brain-tumor-segmentation 。
