Medical Image Segmentation with Dual-Encoding and Multi-Level Feature Adaptive Fusion

Purpose: Accurate segmentation of medical images is critical for disease diagnosis, surgical planning and prognostic assessment. TransUNet, a hybrid CNN-Transformer-based method, extracts local features using CNN and compensates for the lack of long-range dependencies through a self-attention mechanism. However, the initial focus on extracting local features from specific regions impacts the generation of subsequent global features, thus constraining the model’s capacity to effectively capture a broader range of semantic information. Effective integration of local and global features plays a pivotal role in achieving precise and dense prediction. Therefore, we propose a novel hybrid CNN-Transformer-based method aimed at enhancing medical image segmentation. Approach: In this study, a dual-encoder parallel structure is used to enhance the feature representation of the input image. By introducing a multi-scale adaptive feature fusion module, a fine fusion of local features across perceptual domains is realized in the decoding process. The generalized convolutional block attention module helps to increase cross-channel interactions in layers with more channels, thus enabling the fusion of local features and global representations at different resolutions during the decoding process. Results: The proposed method achieves average DSC scores of 79.98%, 84.83% and 85.78% on the Synapse, ISIC2017 and Pediatric Pyelonephritis datasets, respectively. These scores are 2.5%, 0.56% and 0.42% higher than those of TransUNet. The best performance of 91.66% is observed on the ACDC dataset, representing improvements of 2.46% and 7.24% compared to HiFormer and DAE-Former, respectively. Conclusions: The experimental results show that the proposed model has a significant competitive advantage in terms of ACDC image segmentation performance.