多级特征交互Transformer的多器官图像分割

多器官医学图像分割有助于医生做出临床诊断. 针对CNN提取全局特征能力弱, Transformer提取局部特征能力弱, 以及Transformer具有二次方计算复杂度的问题, 提出了用于多器官医学图像分割的多级特征交互Transformer模型. 所提模型采用CNN提取局部特征, 局部特征经Swin Transformer输出全局特征; 通过下采样分别产生多级局部和全局特征, 每级局部和全局特征经过交互并增强; 每级增强后的特征经多级特征融合模块进行交叉融合; 再次融合后的特征经过上采样和分割头输出分割掩码. 所提模型在Synapse和ACDC数据集上进行实验, 平均DSC和平均HD95系数值为80.16%和19.20 mm, 均优于LGNet和RFE-UNet等代表性模型. 该模型对多器官医学图像分割是有效的.

Multi-level Feature Interaction Transformer for Multi-organ Image Segmentation

Clinical diagnoses can be facilitated through the utilization of multi-organ medical image segmentation. This study proposes a multi-level feature interaction Transformer model to address the issues of weak global feature extraction capability in CNN, weak local feature extraction capability in Transformer, and the quadratic computational complexity problem of Transformer for multi-organ medical image segmentation. The proposed model employs CNN for extracting local features, which are then transformed into global features through Swin Transformer. Multi-level local and global features are generated through down-sampling, and each level of local and global features undergo interaction and enhancement. After the enhancement at each level, the features are cross-fused by multi-level feature fusion modules. The features, once again fused, pass through up-sampling and segmentation heads to produce segmentation masks. The proposed model is experimented on the Synapse and ACDC datasets, achieving average dice similarity coefficient (DSC) and average 95th percentile Hausdorff distance (HD95) values of 80.16% and 19.20 mm, respectively. These results outperform representative models such as LGNet and RFE-UNet. The proposed model is effective for multi-organ medical image segmentation.