Pie-UNet: A Novel Parallel Interaction Encoder for Medical Image Segmentation

Most of the initial medical image segmentation methods based on deep learning adopt a full convolutional structure, while the fixed size of the convolutional window limits the modeling of long-range dependencies. ViT has powerful global modelling capabilities, but low-level feature detail is poorly represented. To address the above problems, we propose a novel encoder structure and design a new U-shaped network for medical image segmentation, called Pie-UNet. Firstly, facing the problem of lack of localization in ViT and lack of global perception in CNN, we complement each other by encoding global and local information separately and implementing both in a parallel interaction manner; meanwhile, we propose a network with local structure-aware ViT, called Rwin Transformer, to enhance the local detail representation of ViT itself; in addition, to further refine the local representation, we construct a focal modulator based on large kernels; finally, we propose a pre-fusion approach to optimize the information interaction between heterogeneous structures. The experimental results demonstrate that our proposed Pie-UNet can achieve optimal and accurate segmentation results compared with several existing medical image segmentation methods.