Fourier Channel Attention Powered Lightweight Network for Image Segmentation

Abstract

The accuracy of image segmentation is critical for quantitative analysis. We report a lightweight network FRUNet based on the U-Net, which combines the advantages of Fourier channel attention (FCA Block) and Residual unit to improve the accuracy. FCA Block automatically assigns the weight of the learned frequency information to the spatial domain, paying more attention to the precise high-frequency information of diverse biomedical images. While FCA is widely used in image super-resolution with residual network backbones, its role in semantic segmentation is less explored. Here we study the combination of FCA and U-Net, the skip connection of which can fuse the encoder information with the decoder. Extensive experimental results of FRUNet on three public datasets show that the method outperforms other advanced medical image segmentation methods in terms of using fewer network parameters and improved accuracy. It excels in pathological Section segmentation of nuclei and glands.

Keywords

• Medical image segmentation
• Fourier channel attention
• residual unit
• pathological section
• Clinical and Translational Impact Statement- Medical image segmentation can be used to measure the position and size of human tissues or lesions making the changes of anatomical or pathological structures in the image clearer It plays a vital role in computer-aided diagnosis and intelligent medical treatment At the same time quantitative measurement and analysis of relevant imaging indicators before and after treatment will help doctors diagnose follow up or revise the treatment plan for patients