A feature enhancement network based on image partitioning in a multi-branch encoder-decoder architecture

Semantic segmentation is of great significance in the medical field, as it can help doctors intelligently, quickly, and accurately locate key lesion areas, providing crucial support for the diagnosis, treatment, and recovery of patients. The primary reason existing segmentation networks encounter accuracy bottlenecks is that the size of lesion images limits the network's attention to information, while also causing a lack of image information transmission between encoding and decoding. This study proposes a Multi-branch Partition Feature Enhancement Network (MPFE Net), which is essentially based on our proposed network system, the Multi-Branch Partition-Guided Decoding Network (MPD Net), using an image partition strategy. This approach divides the image at the encoding end and passes it to the decoding end, enabling parallel decoding with branches based on partitions to alleviate the problem of insufficient image information transmission. In terms of module design and optimization, under the processing idea of image partitioning, we have constructed the Bottleneck module Multi-semantic Progressive Interaction Guider (MPIG) for semantic progressive fusion. Furthermore, we have enhanced the ability to extract local information in the Vision Transformer, constructing the Multi-branched Feature Enhancement with Shared ViT (MFES ViT) to enhance control over image details. In the experiments, MPFE Net was compared with 20 models on 8 medical datasets for metric exploration, image comparison, process verification, and statistical analysis. We also discussed in detail 4 key issues. The results show that MPFE Net has better lesion universality and segmentation superiority.