An improved multi-scale feature extraction network for medical image segmentation.

ABSTRACT:

Background

The use of U-Net and its variations has led to significant advancements in medical image segmentation. However, the encoder-decoder structures of these models often lose spatial information during downsampling. Skip connections can help address this issue; however, they may also introduce excessive irrelevant background information. Additionally, medical images display significant scale variations and complex tissue structures, making it challenging for existing models to accurately separate tissues from the background. To address these issues, we developed the Res2Net-ConvFormer-Dilation-UNet (Res2-CD-UNet), a multi-scale feature extraction network for medical image segmentation.

Methods

This study presents a novel U-shaped segmentation network that employs Res2N

SUBMITTER: Guo H 

PROVIDER: S-EPMC11652013 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature