Graph-based Pyramid Global Context Reasoning with a Saliency-aware Projection for COVID-19 Lung Infections Segmentation

• URL: http://arxiv.org/abs/2103.04235v1
• Date: Sun, 7 Mar 2021 02:28:10 GMT
• Title: Graph-based Pyramid Global Context Reasoning with a Saliency-aware Projection for COVID-19 Lung Infections Segmentation
• Authors: Huimin Huang, Ming Cai, Lanfen Lin, Jing Zheng, Xiongwei Mao, Xiaohan Qian, Zhiyi Peng, Jianying Zhou, Yutaro Iwamoto, Xian-Hua Han, Yen-Wei Chen, Ruofeng Tong
• Abstract summary: We propose a Graph-based Pyramid Global Context Reasoning (Graph-PGCR) module. It is capable of modeling long-range dependencies among disjoint infections as well as adapt size variation. Our Graph- PGCR module is plug-and-play, which can be integrated into any architecture to improve its performance.
• Score: 16.94939282349418
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Coronavirus Disease 2019 (COVID-19) has rapidly spread in 2020, emerging a mass of studies for lung infection segmentation from CT images. Though many methods have been proposed for this issue, it is a challenging task because of infections of various size appearing in different lobe zones. To tackle these issues, we propose a Graph-based Pyramid Global Context Reasoning (Graph-PGCR) module, which is capable of modeling long-range dependencies among disjoint infections as well as adapt size variation. We first incorporate graph convolution to exploit long-term contextual information from multiple lobe zones. Different from previous average pooling or maximum object probability, we propose a saliency-aware projection mechanism to pick up infection-related pixels as a set of graph nodes. After graph reasoning, the relation-aware features are reversed back to the original coordinate space for the down-stream tasks. We further con- struct multiple graphs with different sampling rates to handle the size variation problem. To this end, distinct multi-scale long-range contextual patterns can be captured. Our Graph- PGCR module is plug-and-play, which can be integrated into any architecture to improve its performance. Experiments demonstrated that the proposed method consistently boost the performance of state-of-the-art backbone architectures on both of public and our private COVID-19 datasets.

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