A Medical Image Segmentation Network with Multi-Scale and Dual-Branch Attention

Abstract

Accurate medical image segmentation can assist doctors in observing lesion areas and making precise judgments. Effectively utilizing important multi-scale semantic information in local and global contexts is key to improving segmentation accuracy. In this paper, we present a multi-scale dual attention network (MSDA-Net), which enhances feature representation under different receptive fields and effectively utilizes the important multi-scale semantic information from both local and global contexts in medical images. MSDA-Net is a typical encoder–decoder structure and introduces a multi-receptive field densely connected module (MRD) in the decoder. This module captures semantic information across various receptive fields and utilizes dense connections to provide comprehensive and detailed semantic representations. Furthermore, a parallel dual-branch attention module (PDA), incorporating spatial and channel attention, focuses intensively on detailed features within lesion areas. This module enhances feature representation, facilitates the identification of disease boundaries, and improves the accuracy of segmentation. To validate the effectiveness of MSDA-Net, we conducted performance analyses on the CVC-ClinicDB, 2018 Data Science Bowl, ISIC 2018, and colon cancer slice datasets. We also compared our method with U-Net, UNet++, and other methods. The experimental results unequivocally demonstrate that MSDA-Net outperforms these methods, showcasing its superior performance in medical image segmentation tasks.

Keywords

• medical image segmentation
• deep learning
• multi-scale information
• attention mechanism