Open AccessA multi‐class COVID‐19 segmentation network with pyramid attention and edge loss in CT images
Author(s) -
Yu Fuli,
Zhu Yu,
Qin Xiangxiang,
Xin Ying,
Yang Dawei,
Xu Tao
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12249
Subject(s) - segmentation , artificial intelligence , computer science , discriminative model , pyramid (geometry) , image segmentation , pattern recognition (psychology) , covid-19 , computer vision , pooling , convolutional neural network , medicine , infectious disease (medical specialty) , mathematics , disease , pathology , geometry
At the end of 2019, a novel coronavirus COVID‐19 broke out. Due to its high contagiousness, more than 74 million people have been infected worldwide. Automatic segmentation of the COVID‐19 lesion area in CT images is an effective auxiliary medical technology which can quantitatively diagnose and judge the severity of the disease. In this paper, a multi‐class COVID‐19 CT image segmentation network is proposed, which includes a pyramid attention module to extract multi‐scale contextual attention information, and a residual convolution module to improve the discriminative ability of the network. A wavelet edge loss function is also proposed to extract edge features of the lesion area to improve the segmentation accuracy. For the experiment, a dataset of 4369 CT slices is constructed, including three symptoms: ground glass opacities, interstitial infiltrates, and lung consolidation. The dice similarity coefficients of three symptoms of the model achieve 0.7704, 0.7900, 0.8241 respectively. The performance of the proposed network on public dataset COVID‐SemiSeg is also evaluated. The results demonstrate that this model outperforms other state‐of‐the‐art methods and can be a powerful tool to assist in the diagnosis of positive infection cases, and promote the development of intelligent technology in the medical field.