Applied Sciences (Mar 2024)

MRFA-Net: Kidney Segmentation Method Based on Multi-Scale Feature Fusion and Residual Full Attention

  • Junlin Chen,
  • Hongbo Fan,
  • Dangguo Shao,
  • Shuting Dai

DOI
https://doi.org/10.3390/app14062302
Journal volume & issue
Vol. 14, no. 6
p. 2302

Abstract

Read online

For the characterization of the kidney segmentation task, this paper proposes a self-supervised kidney segmentation method based on multi-scale feature fusion and residual full attention, named MRFA-Net. In this study, we introduce the multi-scale feature fusion module to extract multi-scale information of kidneys from abdominal CT slices; additionally, the residual full-attention convolution module is designed to handle the multi-scale information of kidneys by introducing a full-attention mechanism, thus improving the segmentation results of kidneys. The Dice coefficient on the Kits19 dataset reaches 0.972. The experimental results demonstrate that the proposed method achieves good segmentation performance compared to other algorithms, effectively enhancing the accuracy of kidney segmentation.

Keywords