Research progress in lung parenchyma segmentation based on computed tomography_Journal of Biomedical Engineering

Authors：

 XIAO Hanguang ¹ , RAN Zhiqiang ¹ , HUANG Jinfeng ¹ , REN Huijiao ¹ , LIU Chang ¹ , ZHANG Banglin ¹ , ZHANG Bolong ¹ , DANG Jun ²

1. Department of Intelligent Science, School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, P.R.China;
2. Department of Radiotherapy, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, P.R.China;

Corresponding author：

XIAO Hanguang, Email: hgxiao@cqut.edu.cn

Keywords：

computed tomography; lung parenchyma segmentation; deep learning

DOI：

10.7507/1001-5515.202008032

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Lung diseases such as lung cancer and COVID-19 seriously endanger human health and life safety, so early screening and diagnosis are particularly important. computed tomography (CT) technology is one of the important ways to screen lung diseases, among which lung parenchyma segmentation based on CT images is the key step in screening lung diseases, and high-quality lung parenchyma segmentation can effectively improve the level of early diagnosis and treatment of lung diseases. Automatic, fast and accurate segmentation of lung parenchyma based on CT images can effectively compensate for the shortcomings of low efficiency and strong subjectivity of manual segmentation, and has become one of the research hotspots in this field. In this paper, the research progress in lung parenchyma segmentation is reviewed based on the related literatures published at domestic and abroad in recent years. The traditional machine learning methods and deep learning methods are compared and analyzed, and the research progress of improving the network structure of deep learning model is emphatically introduced. Some unsolved problems in lung parenchyma segmentation were discussed, and the development prospect was prospected, providing reference for researchers in related fields.

Citation： XIAO Hanguang, RAN Zhiqiang, HUANG Jinfeng, REN Huijiao, LIU Chang, ZHANG Banglin, ZHANG Bolong, DANG Jun. Research progress in lung parenchyma segmentation based on computed tomography. Journal of Biomedical Engineering, 2021, 38(2): 379-386. doi: 10.7507/1001-5515.202008032 Copy

1.	Henley S J, Ward E M, Scott S, et al. Annual report to the nation on the status of cancer, part I: national cancer statistics. Cancer, 2020, 126(10): 2225-2249.
2.	刘秀玲, 戚帅帅, 熊鹏, 等. 融合多尺度信息的肺结节自动检测算法. 生物医学工程学杂志, 2020, 37(3): 434-441.
3.	王婧璇, 林岚, 赵思远, 等. 基于深度学习的肺结节计算机断层扫描影像检测与分类的研究进展. 生物医学工程学杂志, 2019, 36(4): 670-676.
4.	Ziyad S R, Radha V, Vayyapuri T. Overview of computer aided detection and computer aided diagnosis systems for lung nodule detection in computed tomography. Curr Med Imaging Rev, 2020, 16(1): 16-26.
5.	Xu Mingjie, Qi Shouliang, Yong Yue, et al. Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset. Biomed Eng Online, 2019, 18(1): 2.
6.	Xiao X, Zhao J, Qiang Y, et al. An automated segmentation method for lung parenchyma image sequences based on fractal geometry and convex hull algorithm. Applied Sciences, 2018, 8(5): 832.
7.	Gopalakrishnan S, Kandaswamy A. Automatic delineation of lung parenchyma based on multilevel thresholding and gaussian mixture modelling. CMES, 2018, 114(2): 141-152.
8.	Zhang S, Zhao Y, Bai P. Object Localization improved grabcut for lung parenchyma segmentation. Procedia Computer Science, 2018, 131: 1311-1317.
9.	Kumar S P, Latte M V. Lung parenchyma segmentation: fully automated and accurate approach for thoracic CT scan images. IETE Journal of Research, 2018, 66(3): 370-383.
10.	Peng Tao, Xu T C, Wang Yihuai, et al. Hybrid automatic lung segmentation on chest CT scans. IEEE Access, 2020, 8: 73293-73306.
11.	张华海, 白培瑞, 郭子杨, 等. 一种融合表面波变换与脉冲耦合神经网络的三维肺实质分割算法. 生物医学工程学杂志, 2020, 37(4): 630-640.
12.	Cheimariotis G A, Al-Mashat M, Haris K, et al. Automatic lung segmentation in functional SPECT images using active shape models trained on reference lung shapes from CT. Ann Nucl Med, 2018, 32(2): 94-104.
13.	Chung H, Ko H, Jeon S J, et al. Automatic lung segmentation with Juxta-Pleural nodule identification using active contour model and bayesian approach. IEEE J Transl Eng Health Med, 2018, 6: 1800513.
14.	Chen G, Xiang D, Zhang B, et al. Automatic pathological lung segmentation in low-dose CT image using eigenspace sparse shape composition. IEEE Trans Med Imaging, 2019, 38(7): 1736-1749.
15.	Nithila E E, Kumar S S. Segmentation of lung from CT using various active contour models. Biomedical Signal Processing and Control, 2019, 47: 57-62.
16.	Wang G, Liu X, Li C, et al. A Noise-Robust framework for automatic segmentation of COVID-19 pneumonia lesions from CT images. IEEE Trans Med Imaging, 2020, 39(8): 2653-2663.
17.	Chen Ying, Wang Y, Hu Fei, et al. A lung dense deep convolution neural network for robust lung parenchyma segmentation. IEEE Access, 2020, 8: 93527-93547.
18.	Zhang Q, Zhang M, Chen T, et al. Recent advances in convolutional neural network acceleration. Neurocomputing, 2019, 323: 37-51.
19.	Liu X, Guo S, Yang B, et al. Automatic organ segmentation for CT scans based on super-pixel and convolutional neural networks. J Digit Imaging, 2018, 31(5): 748-760.
20.	Liu C, Pang M. Extracting lungs from CT images via deep convolutional neural network based segmentation and two-pass contour refinement. J Digit Imaging, 2020, 33(6): 1465-1478.
21.	Lateef F, Ruichek Y. Survey on semantic segmentation using deep learning techniques. Neurocomputing, 2019, 338: 321-348.
22.	吴玉超, 林岚, 王婧璇. 基于卷积神经网络的语义分割在医学图像中的应用. 生物医学工程学杂志, 2020, 37(3): 533-540.
23.	Long J, Shelhamer E, Darrell T. Fully Convolutional Networks for Semantic Segmentation//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston: IEEE, 2015. DOI: 10.1109/CVPR.2015.7298965.
24.	Geng L, Zhang S, Tong J, et al. Lung segmentation method with dilated convolution based on VGG-16 network. Comput Assist Surg (Abingdon), 2019, 24(2): 27-33.
25.	Anthimopoulos M, Christodoulidis S, Ebner L, et al. Semantic segmentation of pathological lung tissue with dilated fully convolutional networks. IEEE J Biomed Health Inform, 2019, 23(2): 714-722.
26.	Hofmanninger J, Prayer F, Pan J, et al. Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem. European Radiology Experimental, 2020, 4: 50.
27.	Hu Qinhua, de F. Souza L F, Holanda G B, et al An effective approach for CT lung segmentation using mask region-based convolutional neural networks. Artificial Intelligence in Medicine, 2020, 103: 101792.
28.	Han Tao, Nunes V X, de Freitas Souza L F, et al. Internet of medical things—based on deep learning techniques for segmentation of lung and stroke regions in CT scans. IEEE Access, 2020, 8: 71117-71135.
29.	Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. Medical Image Computing and Computer Assisted Intervention, 2015, arXiv: 1505.04597.
30.	Skourt B A, Hassani A E, Majda A. Lung CT image segmentation using deep neural networks. ScienceDirect, 2018, 127: 109-113.
31.	Park B, Park H, Lee S M, et al. Lung segmentation on HRCT and volumetric CT for diffuse interstitial lung disease using deep convolutional neural networks. J Digit Imaging, 2019, 32(6): 1019-1026.
32.	Tan J, Jing L, Huo Y, et al. LGAN: lung segmentation in CT scans using generative adversarial network. Computerized Medical Imaging and Graphics, 2020, 87: 101817.
33.	Khanna A, Londhe N D, Gupta S, et al. A deep residual U-Net convolutional neural network for automated lung segmentation in computed tomography images. Biocybern Biomed Eng, 2020, 40(3): 1314-1327.
34.	Zhang Z, Wu C, Coleman S, et al. DENSE-inception U-net for medical image segmentation. Comput Methods Programs Biomed, 2020, 192: 105395.
35.	Zhu J, Zhang J, Qiu B, et al. Comparison of the automatic segmentation of multiple organs at risk in CT images of lung cancer between deep convolutional neural network-based and atlas-based techniques. Acta Oncol, 2019, 58(2): 257-264.
36.	Park J, Yun J, Kim N, et al. Fully automated lung lobe segmentation in volumetric chest CT with 3D U-Net: validation with intra- and extra-datasets. J Digit Imaging, 2020, 33(1): 221-230.
37.	Nemoto T, Futakami N, Yagi M, et al. Efficacy evaluation of 2D, 3D U-Net semantic segmentation and atlas-based segmentation of normal lungs excluding the trachea and main bronchi. J Radiat Res, 2020, 61(2): 257-264.
38.	Dong X, Lei Y, Wang T, et al. Automatic multiorgan segmentation in thorax CT images using U-net-GAN. Med Phys, 2019, 46(5): 2157-2168.
39.	Ma J, Wang Y, An X, et al. Towards efficient COVID-19 CT annotation: a benchmark for lung and infection segmentation, arXiv preprint, 2020, arXiv: 2004.12537.

1. Henley S J, Ward E M, Scott S, et al. Annual report to the nation on the status of cancer, part I: national cancer statistics. Cancer, 2020, 126(10): 2225-2249.
2. 刘秀玲, 戚帅帅, 熊鹏, 等. 融合多尺度信息的肺结节自动检测算法. 生物医学工程学杂志, 2020, 37(3): 434-441.
3. 王婧璇, 林岚, 赵思远, 等. 基于深度学习的肺结节计算机断层扫描影像检测与分类的研究进展. 生物医学工程学杂志, 2019, 36(4): 670-676.
4. Ziyad S R, Radha V, Vayyapuri T. Overview of computer aided detection and computer aided diagnosis systems for lung nodule detection in computed tomography. Curr Med Imaging Rev, 2020, 16(1): 16-26.
5. Xu Mingjie, Qi Shouliang, Yong Yue, et al. Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset. Biomed Eng Online, 2019, 18(1): 2.
6. Xiao X, Zhao J, Qiang Y, et al. An automated segmentation method for lung parenchyma image sequences based on fractal geometry and convex hull algorithm. Applied Sciences, 2018, 8(5): 832.
7. Gopalakrishnan S, Kandaswamy A. Automatic delineation of lung parenchyma based on multilevel thresholding and gaussian mixture modelling. CMES, 2018, 114(2): 141-152.
8. Zhang S, Zhao Y, Bai P. Object Localization improved grabcut for lung parenchyma segmentation. Procedia Computer Science, 2018, 131: 1311-1317.
9. Kumar S P, Latte M V. Lung parenchyma segmentation: fully automated and accurate approach for thoracic CT scan images. IETE Journal of Research, 2018, 66(3): 370-383.
10. Peng Tao, Xu T C, Wang Yihuai, et al. Hybrid automatic lung segmentation on chest CT scans. IEEE Access, 2020, 8: 73293-73306.
11. 张华海, 白培瑞, 郭子杨, 等. 一种融合表面波变换与脉冲耦合神经网络的三维肺实质分割算法. 生物医学工程学杂志, 2020, 37(4): 630-640.
12. Cheimariotis G A, Al-Mashat M, Haris K, et al. Automatic lung segmentation in functional SPECT images using active shape models trained on reference lung shapes from CT. Ann Nucl Med, 2018, 32(2): 94-104.
13. Chung H, Ko H, Jeon S J, et al. Automatic lung segmentation with Juxta-Pleural nodule identification using active contour model and bayesian approach. IEEE J Transl Eng Health Med, 2018, 6: 1800513.
14. Chen G, Xiang D, Zhang B, et al. Automatic pathological lung segmentation in low-dose CT image using eigenspace sparse shape composition. IEEE Trans Med Imaging, 2019, 38(7): 1736-1749.
15. Nithila E E, Kumar S S. Segmentation of lung from CT using various active contour models. Biomedical Signal Processing and Control, 2019, 47: 57-62.
16. Wang G, Liu X, Li C, et al. A Noise-Robust framework for automatic segmentation of COVID-19 pneumonia lesions from CT images. IEEE Trans Med Imaging, 2020, 39(8): 2653-2663.
17. Chen Ying, Wang Y, Hu Fei, et al. A lung dense deep convolution neural network for robust lung parenchyma segmentation. IEEE Access, 2020, 8: 93527-93547.
18. Zhang Q, Zhang M, Chen T, et al. Recent advances in convolutional neural network acceleration. Neurocomputing, 2019, 323: 37-51.
19. Liu X, Guo S, Yang B, et al. Automatic organ segmentation for CT scans based on super-pixel and convolutional neural networks. J Digit Imaging, 2018, 31(5): 748-760.
20. Liu C, Pang M. Extracting lungs from CT images via deep convolutional neural network based segmentation and two-pass contour refinement. J Digit Imaging, 2020, 33(6): 1465-1478.
21. Lateef F, Ruichek Y. Survey on semantic segmentation using deep learning techniques. Neurocomputing, 2019, 338: 321-348.
22. 吴玉超, 林岚, 王婧璇. 基于卷积神经网络的语义分割在医学图像中的应用. 生物医学工程学杂志, 2020, 37(3): 533-540.
23. Long J, Shelhamer E, Darrell T. Fully Convolutional Networks for Semantic Segmentation//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston: IEEE, 2015. DOI: 10.1109/CVPR.2015.7298965.
24. Geng L, Zhang S, Tong J, et al. Lung segmentation method with dilated convolution based on VGG-16 network. Comput Assist Surg (Abingdon), 2019, 24(2): 27-33.
25. Anthimopoulos M, Christodoulidis S, Ebner L, et al. Semantic segmentation of pathological lung tissue with dilated fully convolutional networks. IEEE J Biomed Health Inform, 2019, 23(2): 714-722.
26. Hofmanninger J, Prayer F, Pan J, et al. Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem. European Radiology Experimental, 2020, 4: 50.
27. Hu Qinhua, de F. Souza L F, Holanda G B, et al An effective approach for CT lung segmentation using mask region-based convolutional neural networks. Artificial Intelligence in Medicine, 2020, 103: 101792.
28. Han Tao, Nunes V X, de Freitas Souza L F, et al. Internet of medical things—based on deep learning techniques for segmentation of lung and stroke regions in CT scans. IEEE Access, 2020, 8: 71117-71135.
29. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. Medical Image Computing and Computer Assisted Intervention, 2015, arXiv: 1505.04597.
30. Skourt B A, Hassani A E, Majda A. Lung CT image segmentation using deep neural networks. ScienceDirect, 2018, 127: 109-113.
31. Park B, Park H, Lee S M, et al. Lung segmentation on HRCT and volumetric CT for diffuse interstitial lung disease using deep convolutional neural networks. J Digit Imaging, 2019, 32(6): 1019-1026.
32. Tan J, Jing L, Huo Y, et al. LGAN: lung segmentation in CT scans using generative adversarial network. Computerized Medical Imaging and Graphics, 2020, 87: 101817.
33. Khanna A, Londhe N D, Gupta S, et al. A deep residual U-Net convolutional neural network for automated lung segmentation in computed tomography images. Biocybern Biomed Eng, 2020, 40(3): 1314-1327.
34. Zhang Z, Wu C, Coleman S, et al. DENSE-inception U-net for medical image segmentation. Comput Methods Programs Biomed, 2020, 192: 105395.
35. Zhu J, Zhang J, Qiu B, et al. Comparison of the automatic segmentation of multiple organs at risk in CT images of lung cancer between deep convolutional neural network-based and atlas-based techniques. Acta Oncol, 2019, 58(2): 257-264.
36. Park J, Yun J, Kim N, et al. Fully automated lung lobe segmentation in volumetric chest CT with 3D U-Net: validation with intra- and extra-datasets. J Digit Imaging, 2020, 33(1): 221-230.
37. Nemoto T, Futakami N, Yagi M, et al. Efficacy evaluation of 2D, 3D U-Net semantic segmentation and atlas-based segmentation of normal lungs excluding the trachea and main bronchi. J Radiat Res, 2020, 61(2): 257-264.
38. Dong X, Lei Y, Wang T, et al. Automatic multiorgan segmentation in thorax CT images using U-net-GAN. Med Phys, 2019, 46(5): 2157-2168.
39. Ma J, Wang Y, An X, et al. Towards efficient COVID-19 CT annotation: a benchmark for lung and infection segmentation, arXiv preprint, 2020, arXiv: 2004.12537.

Previous Article
Progress of motion artifact correction in photoacoustic microscopy and photoacoustic tomography
Next Article
Mesenchymal stem cells and skin injury repair

Journal of Biomedical Engineering

Research progress in lung parenchyma segmentation based on computed tomography

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content