Medical Image Segmentation Fusing Multi-Scale Semantic and Residual Bottleneck Attention

Abstract

In practical applications, U-Net has a semantic gap between encoder and decoder when using a single convolution kernel and hopping join operations, resulting in reduced generalization performance in the segmentation of different types of medical images. Accordingly, a lightweight and flexible medical image segmentation model based on U-Net structure(LFUNet) is constructed. For encoder and decoder, a Multi-scale Semantic(MS) module is designed, whereby each MS module uses a different small convolutional kernel sequence equivalence instead of a larger convolution kernel for convolution operations to obtain different receptive fields and capture different levels of semantic features. A Residual Bottleneck Attention(RBA) module is established that integrates the residual bottleneck structure and attention mechanism, and the hopping connection can narrow the semantic gap between encoder and decoder after embedding the RBA module, allowing the model to focus on the target region. The small convolution kernel sequence of the MS module and the inverse residual structure of the RBA module have fewer parameters, such that the total number of parameters of LFUNet is only 1/3 of U-Net, which greatly reduces the complexity of the model and improves network operation efficiency. The comparative experimental results on four public biomedical image datasets show that the Jaccard coefficients of LFUNet increased by 3.184 6, 11.936 6, 4.243 8, and 0.114 4 percentage points compared with those of U-Net, exhibiting higher segmentation accuracy and generalization performance.