Skip to main content

Advertisement

SpringerLink
Log in

Identification of pulmonary nodules via CT images with hierarchical fully convolutional networks

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Lung cancer is one of the most diagnosable forms of cancer worldwide. The early diagnoses of pulmonary nodules in computed tomography (CT) chest scans are crucial for potential patients. Recent researches have showed that the methods based on deep learning have made a significant progress for the medical diagnoses. However, the achievements on identification of pulmonary nodules are not yet satisfactory enough to be adopted in clinical practice. It is largely caused by either the existence of many false positives or the heavy time of processing. With the development of fully convolutional networks (FCNs), in this study, we proposed a new method of identifying the pulmonary nodules. The method segments the suspected nodules from their environments and then removes the false positives. Especially, it optimizes the network architecture for the identification of nodules rapidly and accurately. In order to remove the false positives, the suspected nodules are reduced using the 2D models. Furthermore, according to the significant differences between nodules and non-nodules in 3D shapes, the false positives are eliminated by integrating into the 3D models and classified via 3D CNNs. The experiments on 1000 patients indicate that our proposed method achieved 97.78% sensitivity rate for segmentation and 90.1% accuracy rate for detection. The maximum response time was less than 30 s and the average time was about 15 s.

This paper has proposed a new method of identifying the pulmonary nodules. The method segments the suspected nodules from CT images and removes the false positives. As shown in the above, the proposed approach consists of three stages. In stage I, raw data are filtered and normalized. The clean normalized data are then segmented in stage II to extract the suspected nodular lesions through 2D FCNs. Stage III is to remove some false positives generated at stage II via 3D CNNs and outputs the final results.

The experiments on 1000 patients indicate that our proposed method has achieved 97.78% sensitivity rate for segmentation and 90.1% accuracy rate for detection. The maximum response time was less than 30 s and the average time was about 15 s.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Siegel RL, Miller KD, Jemal A (2018) . CA: A Cancer J Clinicians 68(1):7. https://doi.org/10.3322/caac.21442. https://onlinelibrary.wiley.com/doi/abs/10.3322/caac.21442

    Article  Google Scholar 

  2. Trajanovski S, Mavroeidis D, Swisher CL, Gebre BG, Veeling B, Wiemker R, Klinder T, Tahmasebi A, Regis SM, Wald C, McKee BJ, MacMahon H, Pien H (2018) Towards radiologist-level cancer risk assessment in ct lung screening using deep learning. arXiv:1804.01901. Cite arxiv:1804.01901Comment: Submitted for publication. 11 pages. 3 pages supplementary material

  3. Dennison D (2018) 2018 state of lung cancer report. https://www.naaccr.org/2018-state-lung-cancer-report/. NAACC Review

  4. Dey R, Lu Z, Hong Y (2018) Diagnostic classification of lung nodules using 3d neural networks. arXiv:1803.07192. Cite arxiv:1803.07192Comment: Accepted for publication in IEEE International Symposium on Biomedical Imaging (ISBI) 2018 Copyright c 2018 IEEE

  5. Alakwaa W, Nassef M, Badr A (2017) Int J Adv Comput Sci Appl, 8(8)

  6. Sun T, Wang J, Li X, Lv P, Liu F, Luo Y, Gao Q, Zhu H, Guo X (2013) . Comput Methods Programs Biomed 111(2):519. https://doi.org/10.1016/j.cmpb.2013.04.016. http://www.sciencedirect.com/science/article/pii/S0169260713001387

    Article  PubMed  Google Scholar 

  7. Choi W-J, Choi T-S (2014) Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor. Comput Methods Prog Biomed 113(1):37–54. https://doi.org/10.1016/j.cmpb.2013.08.015

    Article  Google Scholar 

  8. Elbaz A, Elnakib A, Abou EM, Gimel’Farb G, Falk R, Farag A (2013) . Int J Biomed Imag 2013(1):517632

    Google Scholar 

  9. Long J, Shelhamer E, Darrell T (2015) In: IEEE Conference on computer vision and pattern recognition, pp 3431–3440

  10. Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) Medical image computing and computer-assisted intervention – MICCAI 2016. In: Ourselin S, Joskowicz L, Sabuncu MR, Unal G, Wells W (eds). Springer International Publishing, Cham, pp 424–432

  11. Oda M, Shimizu N, Oda H, Hayashi Y, Roth HR (2017) Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks. Image Processing

  12. Zhou X, Takayama R, Wang S, Hara T, Fujita H (2017) Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method. Med Phys 44(10):5221–5233. https://doi.org/10.1002/mp.12480

    Article  PubMed  Google Scholar 

  13. Roth HR, Oda H, Hayashi Y, Oda M, Shimizu N, Fujiwara M, Misawa K, Mori K (2017) Hierarchical 3D fully convolutional networks for multi-organ segmentation. CoRR. arXiv:1704.06382

  14. Wu B, Zhou Z, Wang J, Wang Y (2018) In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), pp 1109–1113. https://doi.org/10.1109/ISBI.2018.8363765

  15. Oda M, Shimizu N, Karasawa K, Nimura Y, Kitasaka T, Misawa K, Fujiwara M, Rueckert D, Mori K (2016) Regression forest-based atlas localization and direction specific atlas generation for pancreas segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9901 LNCS: 556–563. https://doi.org/10.1007/978-3-319-46723-8_64

    Google Scholar 

  16. Fosbinder RD (2011) BA RT(R) essentials of radiologic science. LWW

  17. Wright FW (2001) Radiology of the chest and related conditions. CRC Press

  18. Kazerooni EA, Gross BH (2003) The core curriculum: cardiopulmonary imaging (the core curriculum series). LWW

  19. Chan TF, Vese LA (2001) . IEEE Trans Image Process 10(2):266. https://doi.org/10.1109/83.902291

    Article  CAS  PubMed  Google Scholar 

  20. Comaniciu D, Ramesh V, Meer P (2003) . IEEE Trans Pattern Anal Mach Intell 25(5):564. https://doi.org/10.1109/TPAMI.2003.1195991

    Article  Google Scholar 

  21. Tong T, Wolz R, Wang Z, Gao Q, Misawa K, Fujiwara M, Mori K, Hajnal JV, Rueckert D (2015) . Med Image Anal 23(1):92

    Article  PubMed  Google Scholar 

  22. Tianchi (2017) Tianchi medical competition. https://tianchi.aliyun.com/competition/information.htm?spm=5176.100067.5678.2.68877978Zp7JNf&raceId=231601. Accessed 26 Sept (2017)

  23. T. Bvendt01. Lidc-idri (2012). https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI#bb33bb0159ce4700a5a9ad1590697057. Accessed 21 March (2012)

Download references

Acknowledgments

The authors would like to thank the data providers of [23] for the testing data sets.

Funding

This work was partially supported by the Natural Science Foundation of China (No. 61572022) and the Ningbo eHealth Project (No. 2016C11024).

Author information

Authors and Affiliations

  1. Ningbo Institute of Technology, Zhejiang University, Ningbo, 315100, China

    Genlang Chen & Chaoyi Pang

  2. Ningbo Research Institute, Zhejiang University, Ningbo, 315100, China

    Genlang Chen

  3. College of Computer Science, Zhejiang University, Hangzhou, 315100, China

    Jiajian Zhang & Deyun Zhuo

  4. Department of Radiology, Ningbo First Hospital, Ningbo Hospital, Zhejiang University, Ningbo, 315100, China

    Yuning Pan

Authors
  1. Genlang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiajian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Deyun Zhuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuning Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chaoyi Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Genlang Chen.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, G., Zhang, J., Zhuo, D. et al. Identification of pulmonary nodules via CT images with hierarchical fully convolutional networks. Med Biol Eng Comput 57, 1567–1580 (2019). https://doi.org/10.1007/s11517-019-01976-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-019-01976-1

Keywords

Search

Navigation