Abstract: The significant variations in shape and size, along with blurred boundaries and complex tissue structures in glioma Magnetic Resonance Images (MRI), presented a challenging task for brain tumor segmentation. Typically, this task required researchers with specialized expertise to design complex custom network models, which was not only time-consuming but also necessitated considerable human resources. To streamline the network design process and automatically obtain the optimal network architecture, a Neural Architecture Search for multi-tissue segmentation using Convolutional and Transformer-based Networks in glioma segmentation (NASCT-Net) was proposed. This approach aimed to improve segmentation precision during the construction of a network architecture for multi-modal MRI brain tumor segmentation. Firstly, Neural Architecture Search (NAS) technology was applied to the construction of an encoder in NASCT-Net, forming stackable NAS-CNN encoding modules to automatically optimize the network structure for precise glioma segmentation. Secondly, a feature encoding module based on the Transformer was integrated into the lower layers of the encoder to improve the representation of relative positions and global information among tumor components. Lastly, a Volume-Weighted Dice Loss function (VWDiceLoss) was constructed to address the imbalance between foreground and background. NASCT-Net was compared with advanced methods such as Swin-Unet on the Brats2019 brain tumor datasets. The results show an average Dice score improvement of 0.009 and a significant increase in the average Hausdorff distance by 1.831mm. These findings validate the effectiveness of NASCT-Net in enhancing the precision of multi-tissue brain tumor segmentation.

Key words: network architecture, Network Architecture Search（NAS）, brain tumor segmentation, Convolutional Neural Network（CNN）, Transformer

摘要： 脑胶质瘤在磁共振图像（MRI）中的形状大小变化大，边界模糊且组织结构复杂，这些特点导致了脑肿瘤分割任务的挑战性，通常需要具备深厚专业知识的研究人员设计复杂定制网络模型。这一过程不仅耗时，而且需要大量人力资源。为简化网络设计流程，自动获取最优网络结构，提出一种基于卷积和Transformer神经网络架构搜索的脑胶质瘤多组织分割网络——NASCT-Net（Neural Architecture Search for multi-tissue segmentation using Convolutional and Transformer-based Networks in glioma segmentation），在构建用于多模态磁共振成像脑肿瘤分割的网络架构过程中，同步提高分割精确度。首先，NASCT-Net将神经架构搜索（Neural Architecyure Search，NAS）技术应用于编码器的构建，形成可堆叠的NAS编解码模块，以自动优化适用于脑胶质瘤精准分割的网络架构；其次，在编码器底层集成基于Transformer的特征编码模块，以增强对肿瘤各组之间相对位置和全局信息的表征能力；最后，通过构建体积加权Dice损失函数（Volume-Weighted Dice Loss，VWDiceLoss），以解决前景与背景的不平衡问题。NASCT-Net在Brats2019脑肿瘤数据集上与Swin-Unet等先进方法进行比较，平均Dice相似系数提高了0.009，同时平均Hausdorff距离提升了1.831mm。实验结果验证了NASCT-Net在提高脑肿瘤多组织分割精度方面的有效性。

关键词: 网络架构, 神经网络搜索, 脑肿瘤分割, 卷积神经网络, Transformer