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International Journal of Computer Assisted Radiology and Surgery

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05-06-2021 | Original Article

DUDA-Net: a double U-shaped dilated attention network for automatic infection area segmentation in COVID-19 lung CT images

Authors: Feng Xie, Zheng Huang, Zhengjin Shi, Tianyu Wang, Guoli Song, Bolun Wang, Zihong Liu

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 9/2021

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Abstract

Purpose

The global health crisis caused by coronavirus disease 2019 (COVID-19) is a common threat facing all humankind. In the process of diagnosing COVID-19 and treating patients, automatic COVID-19 lesion segmentation from computed tomography images helps doctors and patients intuitively understand lung infection. To effectively quantify lung infections, a convolutional neural network for automatic lung infection segmentation based on deep learning is proposed.

Method

This new type of COVID-19 lesion segmentation network is based on a U-Net backbone. First, a coarse segmentation network is constructed to extract the lung areas. Second, in the encoding and decoding process of the fine segmentation network, a new soft attention mechanism, namely the dilated convolutional attention (DCA) mechanism, is introduced to enable the network to focus on better quantitative information to strengthen the network's segmentation ability in the subtle areas of the lesions.

Results

The experimental results show that the average Dice similarity coefficient (DSC), sensitivity (SEN), specificity (SPE) and area under the curve of DUDA-Net are 87.06%, 90.85%, 99.59% and 0.965, respectively. In addition, the introduction of a cascade U-shaped network scheme and DCA mechanism can improve the DSC by 24.46% and 14.33%, respectively.

Conclusion

The proposed DUDA-Net approach can automatically segment COVID-19 lesions with excellent performance, which indicates that the proposed method is of great clinical significance. In addition, the introduction of a coarse segmentation network and DCA mechanism can improve the COVID-19 segmentation performance.

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Title: DUDA-Net: a double U-shaped dilated attention network for automatic infection area segmentation in COVID-19 lung CT images
Authors: Feng Xie
Zheng Huang
Zhengjin Shi
Tianyu Wang
Guoli Song
Bolun Wang
Zihong Liu
Publication date: 05-06-2021
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 9/2021
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-021-02418-w

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Abstract

Purpose

Method

Results

Conclusion

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