Graph-Based Region and Boundary Aggregation for Biomedical Image Segmentation



Meng, Yanda ORCID: 0000-0001-7344-2174, Zhang, Hongrun, Zhao, Yitian, Yang, Xiaoyun, Qiao, Yihong, MacCormick, Ian JC, Huang, Xiaowei ORCID: 0000-0001-6267-0366 and Zheng, Yalin ORCID: 0000-0002-7873-0922
(2022) Graph-Based Region and Boundary Aggregation for Biomedical Image Segmentation. IEEE TRANSACTIONS ON MEDICAL IMAGING, 41 (3). pp. 690-701.

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: Image segmentation, Feature extraction, Cognition, Task analysis, Semantics, Optical imaging, Optical computing, Region-boundary, graph neural network, segmentation
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User: Symplectic Admin
Date Deposited: 15 Oct 2021 12:14
Last Modified: 18 Jan 2023 21:26
DOI: 10.1109/TMI.2021.3123567
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3140502