College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590 Shandong, People's Republic of China.
College of Computer Science and Engineering, Qufu Normal University, Rizhao, 276827, People's Republic of China.
Phys Med Biol. 2023 May 22;68(11). doi: 10.1088/1361-6560/acd29f.
Accurate segmentation of head and neck (H&N) tumors is critical in radiotherapy. However, the existing methods lack effective strategies to integrate local and global information, strong semantic information and context information, and spatial and channel features, which are effective clues to improve the accuracy of tumor segmentation. In this paper, we propose a novel method called dual modules convolution transformer network (DMCT-Net) for H&N tumor segmentation in the fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) images.The DMCT-Net consists of the convolution transformer block (CTB), the squeeze and excitation (SE) pool module, and the multi-attention fusion (MAF) module. First, the CTB is designed to capture the remote dependency and local multi-scale receptive field information by using the standard convolution, the dilated convolution, and the transformer operation. Second, to extract feature information from different angles, we construct the SE pool module, which not only extracts strong semantic features and context features simultaneously but also uses the SE normalization to adaptively fuse features and adjust feature distribution. Third, the MAF module is proposed to combine the global context information, channel information, and voxel-wise local spatial information. Besides, we adopt the up-sampling auxiliary paths to supplement the multi-scale information.The experimental results show that the method has better or more competitive segmentation performance than several advanced methods on three datasets. The best segmentation metric scores are as follows: DSC of 0.781, HD95 of 3.044, precision of 0.798, and sensitivity of 0.857. Comparative experiments based on bimodal and single modal indicate that bimodal input provides more sufficient and effective information for improving tumor segmentation performance. Ablation experiments verify the effectiveness and significance of each module.We propose a new network for 3D H&N tumor segmentation in FDG-PET/CT images, which achieves high accuracy.
准确的头颈部（H&N）肿瘤分割在放射治疗中至关重要。然而，现有的方法缺乏有效策略来整合局部和全局信息、强语义信息和上下文信息以及空间和通道特征，这些都是提高肿瘤分割准确性的有效线索。在本文中，我们提出了一种名为双模块卷积变换网络（DMCT-Net）的新方法，用于在氟脱氧葡萄糖正电子发射断层扫描/计算机断层扫描（FDG-PET/CT）图像中对头颈部肿瘤进行分割。DMCT-Net 由卷积变换块（CTB）、挤压和激励（SE）池模块以及多注意融合（MAF）模块组成。首先，CTB 通过使用标准卷积、扩张卷积和变换操作来捕获远程依赖关系和局部多尺度感受野信息。其次，为了从不同角度提取特征信息，我们构建了 SE 池模块，它不仅同时提取强语义特征和上下文特征，还使用 SE 归一化自适应地融合特征并调整特征分布。第三，MAF 模块用于结合全局上下文信息、通道信息和体素局部空间信息。此外，我们采用上采样辅助路径来补充多尺度信息。实验结果表明，该方法在三个数据集上的分割性能优于几种先进方法。最佳分割指标得分如下：DSC 为 0.781，HD95 为 3.044，精度为 0.798，灵敏度为 0.857。基于双模态和单模态的对比实验表明，双模态输入为提高肿瘤分割性能提供了更充分和有效的信息。消融实验验证了每个模块的有效性和重要性。我们提出了一种新的用于 FDG-PET/CT 图像中 3D H&N 肿瘤分割的网络，实现了高精度。
