Improved U-Net Network for COVID-19 Image Segmentation

doi:10.3778/j.issn.1002-8331.2010-0207

Abstract

Abstract:

The novel corona virus pneumonia（COVID-19） pandemic is spreading globally. Computerized Tomography（CT） imaging technology plays a vital role in the fight against global COVID-19. When diagnosing new coronary pneumonia, it will be helpful if the new coronary pneumonia focus area can be automatically and accurately segmented from the CT image, the doctor makes a more accurate and quick diagnosis. Aiming at the segmentation problem of new coronary pneumonia lesions, an automatic segmentation method based on the improved U-Net model is proposed. The EfficientNet-B0 network pre-trained on ImageNet is used in the encoder to extract features of effective information. In the decoder, the traditional up-sampling operation is replaced with a DUpsampling structure, in order to fully obtain the detailed feature information of the lesion edge, and finally the accuracy of the segmentation is improved through the integration of model snapshots. The experimental results on the public data set show that the accuracy, recall and Dice coefficients of the proposed algorithm are 84.24%, 80.43% and 85.12%, respectively. Compared with other segmentation networks, this method can effectively segment the neo-coronary pneumonia lesion area and has good segmentation performance.

Key words: COVID-19, U-Net, semantic segmentation

摘要：

新型冠状病毒肺炎（COVID-19）大流行疾病正在全球范围内蔓延。计算机断层扫描（CT）影像技术，在抗击全球 COVID-19 的斗争中起着至关重要的作用，诊断新冠肺炎时，如果能够从CT图像中自动准确分割出新冠肺炎病灶区域，将有助于医生进行更准确和快速的诊断。针对新冠肺炎病灶分割问题，提出基于U-Net改进模型的自动分割方法。在编码器中运用了在 ImageNet 上预训练好的 EfficientNet-B0网络，对有效信息进行特征提取。在解码器中将传统的上采样操作换成DUpsampling结构，以此来充分获取病灶边缘的细节特征信息，最后通过模型快照的集成提高分割的精度。在公开数据集上的实验结果表明，所提算法的准确率、召回率和Dice系数分别为84.24%、80.43%和85.12%，与其他的语义分割算法相比，该方法能有效分割新冠肺炎病灶区域，具有良好的分割性能。

关键词: 新型冠状病毒肺炎（COVID-19）, U-Net, 语义分割

SONG Yao, LIU Jun. Improved U-Net Network for COVID-19 Image Segmentation[J]. Computer Engineering and Applications, 2021, 57(19): 243-251.

宋瑶，刘俊. 改进U-Net的新冠肺炎图像分割方法[J]. 计算机工程与应用, 2021, 57(19): 243-251.

References

[1] THOMPSON R.Pandemic potential of 2019-nCoV[J].The Lancet Infectious Diseases，2020，20（3）：280.
[2] WHO.Statement on the second meeting of the International Health Regulations（2005）Emergency Committee regarding theoutbreak of novel coronavirus（2019-nCoV）[EB/OL].（2020-01-23）[2020-05-02].https：//www.who.int/news-room/detail/23-01-2020-statement-on-the-meeting-ofthe-international-healthregulations-（2005）-emergencycommittee-regardingthe-outbreak-of-novel-coronavirus-（2019-ncov）.
[3] 李士雪，单莹.新型冠状病毒肺炎研究进展述评[J].山东大学学报（医学版），2020，58（3）：19-25.
LI S X，SHAN Y.Review on the research progress of novel coronavirus pneumonia[J].Journal of Shandong University（Health Sciences），2020，58（3）：19-25.
[4] FANG Y，ZHANG H，XIE J，et al.Sensitivity of chest CT for COVID-19：Comparison to RT-PCR[J].Radiology，2020，296（2）：200432.
[5] YE Z，ZHANG Y，WANG Y，et al.Chest CT manifestations of new coronavirus disease 2019（COVID-19）：A pictorial review[J].European Radiology，2020：1-9.
[6] SHARIF RAZAVIAN A，AZIZPOUR H，SULLIVAN J，et al.CNN features off-the-shelf：An astounding baseline for recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2014：806-813.
[7] LONG J，SHELHAMER E，DARRELL T.Fully convolutionalnetworks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2015：3431-3440.
[8] RONNEBERGER O，FISCHER P，BROX T.U-net：Convolutional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention，2015：234-241.
[9] ZHOU Z，SIDDIQUEE M M R，TAJBAKHSH N，et al.Unet++：A nested u-Net architecture for medical image segmentation[M]//Deep learning in medical image analysis and multimodal learning for clinical decision support.Cham：Springer，2018：3-11.
[10] ?I?EK ?，ABDULKADIR A，LIENKAMP S S，et al. 3D U-Net：Learning dense volumetric segmentation from sparse annotation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention，2016：424-432.
[11] OKTAY O，SCHLEMPER J，FOLGOC L L，et al.Attention u-net：Learning where to look for the pancreas[J].arXiv：1804.03999，2018.
[12] ALOM M Z，HASAN M，YAKOPCIC C，et al.Recurrent residual con-volutional neural network based on u-Net（r2u-Net） for medicalimage segmentation[J].arXiv：1802.06955，2018.
[13] GU Z，CHENG J，FU H，et al.Ce-Net：Context encoder network for 2D medical image segmentation[J].IEEE Transactions on Medical Imaging，2019，38（10）：2281-2292.
[14] FAN D P，ZHOU T，JI G P，et al.INF-Net：Automatic COVID?19 lung infection segmentation from CT images[J].arXiv：2004.14133，2020.
[15] MA J，WANG Y，AN X，et al.Towards efficient COVID-19 CT annotation：A benchmark for lung and infection segmentation[J].arXiv：2004.12537，2020.
[16] 钱宝鑫，肖志勇，宋威.改进的卷积神经网络在肺部图像上的分割应用[J].计算机科学与探索，2020，14（8）：1358-1367.
QIAN B X，XIAO Z Y，SONG W.Application of improved convolutional neural network in lung image segmentation[J].Journal of Frontiers of Computer Science and Technology，2020，14（8）：1358-1367.
[17] 钟思华，郭兴明，郑伊能.改进U-Net网络的肺结节分割方法[J].计算机工程与应用，2020，56（17）：203-209.
ZHONG S H，GUO X M，ZHENG Y N.Improved U-Net network for lung nodules segmentation[J].Computer Engineering and Applications，2020，56（17）：203-209.
[18] TAN M，LE Q V.Efficientnet：Rethinking model scaling for convolutional neural networks[J].arXiv：1905.11946，2019.
[19] CHOLLET F.Xception：Deep learning with depthwise separable convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：1251-1258.
[20] HU J，SHEN L，SUN G.Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：7132-7141.
[21] DENG J，DONG W，SOCHER R，et al.Imagenet：A large-scale hierarchical image database[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition，2009：248-255.
[22] TIAN Z，HE T，SHEN C，et al.Decoders matter for semantic segmentation：Data-dependent decoding enables flexible feature aggregation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2019：3126-3135.
[23] COVID-19 CT segmentation dataset[EB/OL].（2020-04-02）[2020-09-13].http：//medicalsegmentation.com/covid19/.
[24] HUANG G，LI Y，PLEISS G，et al.Snapshot ensembles：Train 1，get m for free[J].arXiv：1704.00109，2017.
[25] BADRINARAYANAN V，KENDALL A，CIPOLLA R.Segnet：A deep convolutional encoder-decoder architecture for image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2017，39（12）：2481-2495.
[26] GU Z，CHENG J，FU H，et al.Ce-Net：Context encoder network for 2D medical image segmentation[J].IEEE Transactions on Medical Imaging，2019，38（10）：2281-2292.