UcUNet: A lightweight and precise medical image segmentation network based on efficient large kernel U-shaped convolutional module design

In recent years, precise medical image segmentation methods based on the encoder–decoder structure have attracted much attention, but there are still some limitations. They mainly manifest in two aspects: one is that the network structure becomes increasingly complex in pursuit of segmentation accuracy, and the other is the insufficient multiscale information fusion ability between the encoder and decoder. To address these issues, we designed a novel lightweight precise medical image segmentation network called UcUNet, which has not only a very large receptive field and multiscale information fusion ability but also a low parameter count. Specifically, we first designed an efficient U-shaped convolution module that can increase the network depth with fewer parameters and effectively filter out invalid features while fusing shallow and deep features. Furthermore, we ingeniously introduced large-kernel convolution into this module, which significantly improved the network’s receptive field with very few parameters, providing new ideas for the use of large-kernel convolution in lightweight models. In addition, we designed a self-adjusting multiscale feature fusion module based on large-kernel convolution to replace the original simple skip connections, effectively enhancing the network’s multiscale information extraction and fusion ability. Finally, we conducted experiments on multiple datasets, and the results showed that our method can achieve precise medical image segmentation with a low parameter count.