Prof. Lingna Chen Profile

Prof. Lingna Chen

at Univ of South China

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Proceedings Article | 23 May 2022 Paper

LCFNet: A lightweight context fusion network for COVID-19 segmentation

Qing Wang, Weijie Hu, Jiayi Wang, Lingli Luo, Xiaolong Ge, Junxi Chen, Lingna Chen

Proceedings Volume 12254, 122541R (2022) https://doi.org/10.1117/12.2640063

KEYWORDS: Image segmentation, Information fusion, Data modeling, Performance modeling, Surface plasmons, Computer programming, Medical imaging, Computed tomography, Visualization, Image fusion

Read Abstract +

The rapid and uncontrollable spread of COVID-19 has seriously threatened global public health. Rapid and accurate diagnosis of COVID-19 is the main key to control and manage the epidemic. COVID-19 segmentation can provide great insights to accelerate clinical decisions. COVID-19 segmentation based on deep learning method has attracted extensive research in the field of medical image analysis. However, most existing networks are heavyweight networks, which causes structural redundancy and expensive computational cost. Moreover, the segmentation problem of low-contrast COVID-19 image and the obscure boundary between the infected area and normal tissues affects accuracy of segmentation. To solve the problems, we propose a lightweight contextual information fusion network, LCFNet, for COVID-19 segmentation. We introduce a contextual information fusion strategy combining multiple global pyramid guidance (GPG) with scale-aware pyramid fusion (SAPF) module and deep supervision (DS) module, which can capture more fine-grained image features. We conduct experiments on two COVID-19 datasets. We perform the ablation studies, demonstrating the effectiveness of key components of the proposed method. Compared with traditional segmentation methods, LCFNet model match more consistently with the ground-truth boundary, which shows the superiority of the proposed method. Moreove, LCFNet has 1.68M parameters, which demonstrates the robustness of the proposed method. Our proposed method can further induce the number of model parameters. Compared with the state-of-the-art methods, the proposed approach has achieved significant improvements and is also superior to other segmentation methods.

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