CNN-GCN aggregation enabled boundary regression for biomedical image segmentation

Yanda Meng, Meng Wei, Dongxu Gao, Yitian Zhao, Xiaoyun Yang, Xiaowei Huang, Yalin Zheng*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Accurate segmentation of anatomic structure is an essential task for biomedical image analysis. Recent popular object contours regression based segmentation methods have increasingly attained researchers’ attentions. They made a new starting point to tackle segmentation tasks instead of commonly used dense pixels classification methods. However, because of the nature of CNN based network (lack of spatial information) and the difficulty of this methodology itself (need of more spatial information), these methods needed extra process to maintain more spatial features, which may cause longer inference time or tedious design and inference process. To address the issue, this paper proposes a simple, intuitive deep learning based contour regression model. We develop a novel multi-level, multi-stage aggregated network to regress the coordinates of the contour of instances directly in an end-to-end manner. The proposed network seamlessly links convolution neural network (CNN) with Attention Refinement module (AR) and Graph Convolution Network (GCN). By hierarchically and iteratively combining features over different layers of the CNN, the proposed model obtains sufficient low-level features and high-level semantic information from the input image. Besides, our model pays distinct attention to the objects’ contours with the help of AR and GCN. Primarily, thanks to the proposed aggregated GCN and vertices sampling method, our model benefits from direct feature learning of the objects’ contour locations from sparse to dense and the spatial information propagation across the whole input image. Experiments on the segmentation of fetal head (FH) in ultrasound images and of the optic disc (OD) and optic cup (OC) in color fundus images demonstrate that our method outperforms state-of-the-art methods in terms of effectiveness and efficiency.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
Number of pages11
ISBN (Electronic)9783030597191
ISBN (Print)9783030597184
Publication statusPublished - 29 Sept 2020
Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 4 Oct 20208 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020


  • aggregation
  • attention
  • GCN
  • regression
  • segmentation
  • UKRI
  • EP/R014094/1


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