ERDUnet: An Efficient Residual Double-Coding Unet for Medical Image Segmentation

Hao Li, Di Hua Zhai*, Yuanqing Xia

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Medical image segmentation is widely used in clinical diagnosis, and methods based on convolutional neural networks have been able to achieve high accuracy. However, it is still difficult to extract global context features, and the parameters are too large to be clinically applied. In this regard, we propose a novel network structure to improve the traditional encoder-decoder network model, which saves parameters while maintaining segmentation accuracy. We improve the feature extraction efficiency by constructing an encoder module that can simultaneously extract local features and global continuity information. A novel attention module is designed to optimize segmentation boundary regions while improving training efficiency. The feature transfer structure of the decoding part is also improved, which fully integrates the features of different levels to restore the spatial resolution more finely. We evaluate our model on seven different medical segmentation datasets, the 2018 Data Science Bowl Challenge (DSBC2018), the 2018 Lesion Boundary Segmentation Challenge (ISIC2018), the Gland Segmentation in Colon Histology Images Challenge (GlaS), Kvasir-SEG, CVC-ClinicDB, Kvasir-Instrument and Polypgen. Extensive experimental results show that our model can achieve good segmentation performance while maintaining a small number of parameters and computational load, which can further facilitate the generalization of the theoretical approach to clinical practice. Our code will be released at https://github.com/caijilia/ERDUnet.

Original languageEnglish
Pages (from-to)2083-2096
Number of pages14
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume34
Issue number4
DOIs
Publication statusPublished - 1 Apr 2024

Keywords

  • Medical image segmentation
  • convolutional neural network
  • deep learning
  • encoder-decoder network
  • reduce parameter scale

Fingerprint

Dive into the research topics of 'ERDUnet: An Efficient Residual Double-Coding Unet for Medical Image Segmentation'. Together they form a unique fingerprint.

Cite this