ResDAC-Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels

Zhanlin Ji, Jianuo Liu, Juncheng Mu, Haiyang Zhang, Chenxu Dai, Na Yuan*, Ivan Ganchev*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The pancreas not only is situated in a complex abdominal background but is also surrounded by other abdominal organs and adipose tissue, resulting in blurred organ boundaries. Accurate segmentation of pancreatic tissue is crucial for computer-aided diagnosis systems, as it can be used for surgical planning, navigation, and assessment of organs. In the light of this, the current paper proposes a novel Residual Double Asymmetric Convolution Network (ResDAC-Net) model. Firstly, newly designed ResDAC blocks are used to highlight pancreatic features. Secondly, the feature fusion between adjacent encoding layers fully utilizes the low-level and deep-level features extracted by the ResDAC blocks. Finally, parallel dilated convolutions are employed to increase the receptive field to capture multiscale spatial information. ResDAC-Net is highly compatible to the existing state-of-the-art models, according to three (out of four) evaluation metrics, including the two main ones used for segmentation performance evaluation (i.e., DSC and Jaccard index). Graphical abstract: (Figure presented.).

Original languageEnglish
Pages (from-to)2087-2100
Number of pages14
JournalMedical and Biological Engineering and Computing
Volume62
Issue number7
DOIs
Publication statusAccepted/In press - 2024

Keywords

  • Image segmentation
  • Medical image processing
  • Pancreatic segmentation
  • ResDAC-Net

Fingerprint

Dive into the research topics of 'ResDAC-Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels'. Together they form a unique fingerprint.

Cite this