Fourier Channel Attention Powered Lightweight Network for Image Segmentation

Zou，Fu; Liu，Yuanhua; Chen，Zelyu; Zhanghao，Karl; Jin，Dayong

2023

10.1109/JTEHM.2023.3262841

IEEE Journal of Translational Engineering in Health and Medicine   影响因子和分区

2168-2372

2168-2372

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.

Biomedical imaging Convolution Convolutional neural networks Data mining Feature extraction Fourier channel attention Frequency-domain analysis Image segmentation Medical image segmentation pathological section Residual unit

SCI ; EI

英语

第一

WOS:000970501700001

20231413850682

Convolution ; Data mining ; Fourier transforms ; Frequency domain analysis ; Image analysis ; Image enhancement ; Medical imaging ; Neural networks ; Semantic Segmentation ; Semantics

Biomedical Engineering:461.1 ; Information Theory and Signal Processing:716.1 ; Data Processing and Image Processing:723.2 ; Artificial Intelligence:723.4 ; Imaging Techniques:746 ; Mathematical Transformations:921.3

2-s2.0-85151537522

Scopus

Department of Biomedical Engineering, UTS-SUStech Joint Research Centre for Biomedical Materials and Devices, Southern University of Science and Technology, Shenzhen, Guangdong, China

Zou，Fu,Liu，Yuanhua,Chen，Zelyu,et al. Fourier Channel Attention Powered Lightweight Network for Image Segmentation[J]. IEEE Journal of Translational Engineering in Health and Medicine,2023,11:252-260.

Zou，Fu,Liu，Yuanhua,Chen，Zelyu,Zhanghao，Karl,&Jin，Dayong.(2023).Fourier Channel Attention Powered Lightweight Network for Image Segmentation.IEEE Journal of Translational Engineering in Health and Medicine,11,252-260.

Zou，Fu,et al."Fourier Channel Attention Powered Lightweight Network for Image Segmentation".IEEE Journal of Translational Engineering in Health and Medicine 11(2023):252-260.