COVID Edge-Net: Automated COVID-19 Lung Lesion Edge Detection in Chest CT Images

Coronavirus Disease 2019 (COVID-19) has been spreading rapidly, threatening global health. Computer-aided screening on chest computed tomography (CT) images using deep learning, especially, lesion segmentation, is an effective complement for COVID-19 diagnosis. Although edge detection highly benefits lesion segmentation, an independent COVID-19 edge detection task in CT scans has been unprecedented and faces several difficulties, e.g., ambiguous boundaries, noises and diverse edge shapes. To this end, we propose the first COVID-19 lesion edge detection model: COVID Edge-Net, containing one edge detection backbone and two new modules: the multi-scale residual dual attention (MSRDA) module and the Canny operator module. MSRDA module helps capture richer contextual relationships for obtaining better deep learning features, which are fused with Canny features from Canny operator module to extract more accurate, refined, clearer and sharper edges. Our approach achieves the state-of-the-art performance and can be a benchmark for COVID-19 edge detection. Code related to this paper is available at: https://github.com/Elephant-123/COVID-Edge-Net. © 2021, Springer Nature Switzerland AG.

[1]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[2]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Qilong Wang,et al.  ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Su Ruan,et al.  An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism , 2020, ArXiv.

[5]  Matthijs Oudkerk,et al.  Diagnosis, Prevention, and Treatment of Thromboembolic Complications in COVID-19: Report of the National Institute for Public Health of the Netherlands , 2020, Radiology.

[6]  Robust chest CT image segmentation of COVID-19 lung infection based on limited data , 2021, Informatics in Medicine Unlocked.

[7]  G. Gao,et al.  A Novel Coronavirus from Patients with Pneumonia in China, 2019 , 2020, The New England journal of medicine.

[8]  Ming-Yu Liu,et al.  CASENet: Deep Category-Aware Semantic Edge Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  P. Horby,et al.  A novel coronavirus outbreak of global health concern , 2020, The Lancet.

[10]  Ben Glocker,et al.  Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images , 2018, Medical Image Anal..

[11]  Nima Tajbakhsh,et al.  UNet++: A Nested U-Net Architecture for Medical Image Segmentation , 2018, DLMIA/ML-CDS@MICCAI.

[12]  Iñaki Soto Rey,et al.  Automated Chest CT Image Segmentation of COVID-19 Lung Infection based on 3D U-Net , 2020, ArXiv.

[13]  Yuan Hu,et al.  Dynamic Feature Fusion for Semantic Edge Detection , 2019, IJCAI.

[14]  K. Yuen,et al.  Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review , 2020, Radiology. Cardiothoracic imaging.

[15]  Jing Xu,et al.  MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation , 2020, AAAI.

[16]  Loïc Le Folgoc,et al.  Attention U-Net: Learning Where to Look for the Pancreas , 2018, ArXiv.

[17]  Olaf Ronneberger,et al.  Invited Talk: U-Net Convolutional Networks for Biomedical Image Segmentation , 2017, Bildverarbeitung für die Medizin.

[18]  Dijia Wu,et al.  Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning , 2020, IEEE Transactions on Medical Imaging.

[19]  Zhongchao Shi,et al.  Does Non-COVID19 Lung Lesion Help? Investigating Transferability in COVID-19 CT Image Segmentation , 2020, 2006.13877.

[20]  Q. Tao,et al.  Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases , 2020, Radiology.

[21]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[22]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  In-So Kweon,et al.  CBAM: Convolutional Block Attention Module , 2018, ECCV.

[24]  Lina Yao,et al.  Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images , 2020, ArXiv.

[25]  Ling Shao,et al.  Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images , 2020, IEEE Transactions on Medical Imaging.

[26]  Hongbing Lu,et al.  Prior-Attention Residual Learning for More Discriminative COVID-19 Screening in CT Images , 2020, IEEE Transactions on Medical Imaging.

[27]  Chi-Wing Fu,et al.  H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes , 2018, IEEE Transactions on Medical Imaging.

[28]  Yaozong Gao,et al.  Lung Infection Quantification of COVID-19 in CT Images with Deep Learning , 2020, ArXiv.

[29]  Yicheng Fang,et al.  Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR , 2020, Radiology.