Lung parenchyma segmentation based on double scale parallel attention network_Journal of Biomedical Engineering

Authors：

FENG Kaili ¹ , REN Lili ² , WU Yanlin ¹ , LI Yan ¹ , WANG Hongrui ¹ ,  WANG Guanglei ¹

1. School of Electronic Information Engineering, Hebei University, Baoding, Hebei 071002, P. R. China;
2. Affiliated Hospital of Hebei University, Baoding, Hebei 071002, P. R. China;

Corresponding author：

WANG Guanglei, Email: 513197133@qq.com

Keywords：

Lung parenchymal segmentation; Deep learning; Collaborative attention; Multi-scale information

DOI：

10.7507/1001-5515.202108005

Video：

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[Abstract ]Automatic and accurate segmentation of lung parenchyma is essential for assisted diagnosis of lung cancer. In recent years, researchers in the field of deep learning have proposed a number of improved lung parenchyma segmentation methods based on U-Net. However, the existing segmentation methods ignore the complementary fusion of semantic information in the feature map between different layers and fail to distinguish the importance of different spaces and channels in the feature map. To solve this problem, this paper proposes the double scale parallel attention (DSPA) network (DSPA-Net) architecture, and introduces the DSPA module and the atrous spatial pyramid pooling (ASPP) module in the “encoder-decoder” structure. Among them, the DSPA module aggregates the semantic information of feature maps of different levels while obtaining accurate space and channel information of feature map with the help of cooperative attention (CA). The ASPP module uses multiple parallel convolution kernels with different void rates to obtain feature maps containing multi-scale information under different receptive fields. The two modules address multi-scale information processing in feature maps of different levels and in feature maps of the same level, respectively. We conducted experimental verification on the Kaggle competition dataset. The experimental results prove that the network architecture has obvious advantages compared with the current mainstream segmentation network. The values of dice similarity coefficient (DSC) and intersection on union (IoU) reached 0.972 ± 0.002 and 0.945 ± 0.004, respectively. This paper achieves automatic and accurate segmentation of lung parenchyma and provides a reference for the application of attentional mechanisms and multi-scale information in the field of lung parenchyma segmentation.

Citation： FENG Kaili, REN Lili, WU Yanlin, LI Yan, WANG Hongrui, WANG Guanglei. Lung parenchyma segmentation based on double scale parallel attention network. Journal of Biomedical Engineering, 2022, 39(4): 721-729. doi: 10.7507/1001-5515.202108005 Copy

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1. Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 2012, 25: 1097-1105.
2. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv, 2014: 1409.1556.
3. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015: 1-9.
4. He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016, 90: 770-778.
5. Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation//International Conference on Medical image computing and computer-assisted intervention, Springer Cham, 2015: 234-241.
6. Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell, 2017, 39(12): 2481-2495.
7. Shaziya H, Shyamala K, Zaheer R. Automatic lung segmentation on thoracic CT scans using U-Net convolutional network//2018 International Conference on Communication and Signal Processing (ICCSP), 2018: 0643-0647.
8. Gu Yuchong, Lai Yaoming, Xie Peiliang, et al. Multi-scale prediction network for lung segmentation//2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), IEEE, 2019: 438-442.
9. Gholamiankhah F, Mostafapour S, Goushbolagh N A, et al. Automated lung segmentation from CT images of normal and COVID-19 pneumonia patients. arXiv preprint arXiv, 2021: 2104.02042.
10. Hu Jie, Shen Li, Sun Gang. Squeeze-and-excitation networks//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 7132-7141.
11. Woo S, Park J, Lee J Y, et al. Cbam: convolutional block attention module//Proceedings of the European conference on computer vision (ECCV), 2018: 3-19.
12. Hu Jie, Shen Li, Albanie S, et al. Gather-excite: exploiting feature context in convolutional neural networks. NIPS'18: Proceedings of the 32nd International Conference on Neural Information Processing Systems, 2018: 9423-9433.
13. Linsley D, Scheibler D, Eberhardt S, et al. Global-and-local attention networks for visual recognition. Benefits, 2018, 64: 01.
14. Tay C P, Roy S, Yap K H. AANet: attribute attention network for person re-identifications//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019: 7127-7136.
15. Misra D, Nalamada T, Arasanipalai A U, et al. Rotate to attend: convolutional triplet attention module//2021 IEEE Winter Conference on Applications of Computer Vision (WACV), 2021: 3138-3147.
16. Wang Xiaolong, Girshick R, Gupta A, et al. Non-local Neural Networks//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 7794-7803.
17. Cao Yue, Xu Jiarui, Lin S, et al. GCNet: non-local networks meet squeeze-excitation networks and beyond//2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019: 1971-1980.
18. Chen Y, Kalantidis Y, Li J, et al. A2-nets: Double attention networks. Advances in neural information processing systems, 2018: 350-359.
19. Liu Jiangjiang, Hou Qibin, Cheng Mingming, et al. Improving convolutional networks with self-calibrated convolutions//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020: 10093-10102.
20. Gao Zilin, Xie Jiangtao, Wang Qilong, et al. Global second-order pooling convolutional networks//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019: 3019-3028.
21. Huang Zilong, Wang Xinggang, Huang Lichao, et al. CCNet: criss-cross attention for semantic segmentation//2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019: 603-612.
22. Fang X, Yan P. Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction. IEEE Trans Med Imaging, 2020, 39(11): 3619-3629.
23. Fu Huazhu, Cheng Jun, Xu Yanwu, et al. Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Trans Med Imaging, 2018, 37(7): 1597-1605.
24. Gu Zaiwang, Cheng Jun, Fu Huazhu, et al. Ce-net: context encoder network for 2D medical image segmentation. IEEE Trans Med Imaging, 2019, 38(10): 2281-2292.
25. Yu Changqian, Wang Jingbo, Peng Chao, et al. Learning a discriminative feature network for semantic segmentation//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 1857-1866.
26. Zhang Z, Zhang X, Peng C, et al. Exfuse: enhancing feature fusion for semantic segmentation// Proceedings of the European Conference on Computer Vision (ECCV), 2018: 269-284.
27. Zhang Pingping, Liu Wei, Lei Yinjie, et al. Cascaded context pyramid for full-resolution 3D semantic scene completion//2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019: 7800-7809.
28. Hou Qibin, Zhou Daquan, Feng Jiashi. Coordinate attention for efficient mobile network design//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021: 13708-13717.
29. Selvaraju R R, Cogswell M, Das A, et al. Grad-CAM: visual explanations from deep networks via gradient-based localization//2017 IEEE International Conference on Computer Vision (ICCV), 2017: 618-626.
30. Chen L C, Papandreou G, Schroff F, et al. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv, 2017: 1706.05587.

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