Graph-based region and boundary aggregation for biomedical image segmentation

Yanda Meng, Hongrun Zhang, Yitian Zhao*, Xiaoyun Yang, Yihong Qiao, Ian J.C. Maccormick, Xiaowei Huang, Yalin Zheng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Segmentation is a fundamental task in biomedical image analysis. Unlike the existing region-based dense pixel classification methods or boundary-based polygon regression methods, we build a novel graph neural network (GNN) based deep learning framework with multiple graph reasoning modules to explicitly leverage both region and boundary features in an end-To-end manner. The mechanism extracts discriminative region and boundary features, referred to as initialized region and boundary node embeddings, using a proposed Attention Enhancement Module (AEM). The weighted links between cross-domain nodes (region and boundary feature domains) in each graph are defined in a data-dependent way, which retains both global and local cross-node relationships. The iterative message aggregation and node update mechanism can enhance the interaction between each graph reasoning module's global semantic information and local spatial characteristics. Our model, in particular, is capable of concurrently addressing region and boundary feature reasoning and aggregation at several different feature levels due to the proposed multi-level feature node embeddings in different parallel graph reasoning modules. Experiments on two types of challenging datasets demonstrate that our method outperforms state-of-The-Art approaches for segmentation of polyps in colonoscopy images and of the optic disc and optic cup in colour fundus images. The trained models will be made available at: https://github.com/smallmax00/Graph_Region_Boudnary

Original languageEnglish
Pages (from-to)690-701
Number of pages12
JournalIEEE Transactions on Medical Imaging
Volume41
Issue number3
Early online date29 Oct 2021
DOIs
Publication statusPublished - 1 Mar 2022

Keywords / Materials (for Non-textual outputs)

  • Graph neural network
  • Region-boundary
  • Segmentation

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