Automatic Segmentation of COVID-19 CT Images using improved MultiResUNet

被引：8

作者：

Yang, Qi ^{[1
]}

Li, Yunke ^{[1
]}

Zhang, Mengyi ^{[1
]}

Wang, Tian ^{[2
]}

Yan, Fei ^{[3
,4
,5
]}

Xie, Chao ^{[3
,4
,5
]}

机构：

[1] Nanjing Tech Univ, Coll Elect Engn & Control Sci, Nanjing, Peoples R China

[2] Beihang Univ, Res Inst Artificial Intelligence, Beijing, Peoples R China

[3] Nanjing Med Univ, Jiangsu Canc Hosp, Nanjing, Peoples R China

[4] Nanjing Med Univ, Jiangsu Inst Canc Res, Nanjing, Peoples R China

[5] Nanjing Med Univ, Affiliated Canc Hosp, Nanjing, Peoples R China

来源：

2020 CHINESE AUTOMATION CONGRESS (CAC 2020) | 2020年

基金：

中国国家自然科学基金; 北京市自然科学基金;

关键词：

COVID-19; MultiResUNet; CT image; Deep learning; Segmentation;

D O I：

10.1109/CAC51589.2020.9327668

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Corona Virus Disease 2019 (COVID-19) has seriously threatened human life and health in just a few months. The global economy, education, transportation and other aspects have been affected. In order to solve the problems caused by COVID-19 as soon as possible, it is important to quickly and accurately confirm whether people are infected. In this paper, we take MultiResUNet as the basic model, introduce a new "Residual block" structure in the encoder part, add Regularization and Dropout to prevent training overfilling, and change the partial activation function. Propose a model suitable for COVID-19 CT image sets, which can automatically segment four parts of COVID-19 CT images (left&right lung, disease and background) by deep learning. The segmentation results are evaluated and the expected results are achieved. It is helpful for medical workers to recognize the infection area quickly.

引用

页码：1614 / 1618

页数：5

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