Skip to main content

Advertisement

SpringerLink
Log in

Automated labeling of the airway tree in terms of lobes based on deep learning of bifurcation point detection

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

Abstract

This paper presents an automatic lobe-based labeling of airway tree method, which can detect the bifurcation points for reconstructing and labeling the airway tree from a computed tomography image. A deep learning-based network structure is designed to identify the four key bifurcation points. Then, based on the detected bifurcation points, the entire airway tree is reconstructed by a new region-growing method. Finally, with the basic airway tree anatomy and topology knowledge, individual branches of the airway tree are classified into different categories in terms of pulmonary lobes. There are several advantages in our method such as the detection of the bifurcation points does not depend on the segmentation of airway tree and only four bifurcation points need to be manually labeled for each sample to prepare the training dataset. The segmentation of airway tree is guided by the detected points, which overcomes the difficulty of manual seed selection of conventional region-growing algorithm. In addition, the bifurcation points can help analyze the tree structure, which provides a basis for effective airway tree labeling. Experimental results show that our method is fast, stable, and the accuracy of our method is 97.85%, which is higher than that of the traditional skeleton-based method.

The pipeline of our proposed lobe-based airway tree labeling method. Given a raw CT volume, a neural network structure is designed to predict major bifurcation points of airway tree. Based on the detected points, airway tree is reconstructed and labeled in terms of lobes

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Miller RD, Hyatt RE (1973) Evaluation of obstructing lesions of the trachea and larynx by flow-volume loops. Am Rev Respir Dis 108(3):475–481

    CAS  PubMed  Google Scholar 

  2. Kiraly AP, Higgins WE, McLennan G, Hoffman EA, Reinhardt JM (2002) Three-dimensional human airway segmentation methods for clinical virtual bronchoscopy. Acad Radiol 9:1153–1168

    Article  PubMed  Google Scholar 

  3. Ramírez E, Sánchez C, Borràs A, Diez-Ferrer M, Rosell A, Gil D (2018) Image-based bronchial anatomy codification for biopsy guiding in video bronchoscopy. In: OR 2.0 context-aware operating theaters, computer assisted robotic endoscopy, clinical image-based procedures, and skin image analysis, pp. 214–222

  4. Gu S, Wang Z, Siegfried JM, Wilson D, Bigbee WL, Pu J (2012) Automated lobe-based airway labeling. In J Biomed Imag 2012:382806

    Google Scholar 

  5. Ukil S, Reinhardt J M (2009) Anatomy-guided lung lobe segmentation in X-ray CT images. IEEE Trans Med Imag 28(2):202–214

    Article  Google Scholar 

  6. Doel T, Gavaghan DJ, Grau V (2015) Review of automatic pulmonary lobe segmentation methods from CT. Comput Med Imaging Graph 40:13–29

    Article  PubMed  Google Scholar 

  7. Bragman FJS, McClelland JR, Jacob J, Hurst JR, Hawkes DJ (2017) Pulmonary lobe segmentation with probabilistic segmentation of the fissures and a groupwise fissure prior. IEEE Trans Med Imag 36 (8):1650–1663

    Article  Google Scholar 

  8. Kumar SN, Beno MM (2012) Segmentation of lung lobes and fissures for surgical pre planning. Int J Comput Appl 51(9):546–550

    Google Scholar 

  9. George K, Harrison AP, Jin D, Xu Z, Mollura DJ (2017) Pathological pulmonary lobe segmentation from CT images using progressive holistically nested neural networks and random walker. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer International Publishing, Cham, pp 195–203

  10. Yu M, Liu H, Gong J, Jin R, Han P, Song E (2014) Automatic segmentation of pulmonary fissures in computed tomography images using 3d surface features. J Digit Imaging 27(1):58–67

    Article  PubMed  Google Scholar 

  11. Gerard SE, Patton TJ, Christensen GE, Bayouth JE, Reinhardt JM (2018) Fissurenet: a deep learning approach for pulmonary fissure detection in CT images. IEEE Trans Med Imag 38(1):156– 166

    Article  Google Scholar 

  12. Kitasaka T, Nakada Y, Mori K, Suenaga Y, Mori M, Takabatake H, Natori H (2006) Recognition of lung lobes and its application to the bronchial structure analysis. In: International conference on pattern recognition, vol 3, pp 288–291

  13. Giuliani N, Payer C, Pienn M, Olschewski H, Urschler M (2018) Pulmonary Lobe Segmentation in CT Images using Alpha-Expansion. In: VISIGRAPP (4: VISAPP), pp. 387–394

  14. Lee M, Lee JG, Kim N, Seo JB, Lee SM (2018) Hybrid airway segmentation using multi-scale tubular structure filters and texture analysis on 3D chest CT scans. J Digit Imaging, pp.1–14

  15. Charbonnier JP, Emv R, Aaa S, Schaefer-Prokop CM, Ginneken BV, Ciompi F (2017) Improving airway segmentation in computed tomography using leak detection with convolutional networks. Med Image Anal 36:52–60

    Article  PubMed  Google Scholar 

  16. Nan Y, Xiao-Min X, Yan L, Jun-Chao M, Jun-Gang G, Chen-Wang J, You-Min G (2015) Effect of computed tomography dose on quantitative measurement and automated segmentation of airway tree. J Med Imag Health In 5(7):1519–1523

    Google Scholar 

  17. Nadeem SA, Hoffman EA, Saha PK (2019) A fully automated CT-based airway segmentation algorithm using deep learning and topological leakage detection and branch augmentation approaches. In: Medical imaging 2019: image processing. international society for optics and photonics, vol. 10949, pp. 109490C

  18. Bauer C, Eberlein M, Beichel RR (2016) Airway tree reconstruction in expiration chest CT scans facilitated by information transfer from corresponding inspiration scans. Med Phys 43(3):1312–1323

    Article  PubMed  PubMed Central  Google Scholar 

  19. Charbonnier JP, Rikxoort EM, Setio AA, Schaefer-Prokop CM, Ginneken BV, Ciompi F (2017) Improving airway segmentation in computed tomography using leak detection with convolutional networks. Med Image Anal 36:52–60

    Article  PubMed  Google Scholar 

  20. Postolski M (2013) Discrete topology and geometry algorithms for quantitative human airway trees analysis based on computed tomography images. PhD Thesis, Paris Est

  21. Jia Y, Ji X, He T, Yu Y, Yu N, Duan H, Guo Y (2018) Quantitative analysis of airway tree in low-dose chest CT with a new model-based iterative reconstruction algorithm: comparison to adaptive statistical iterative reconstruction in routine-dose CT. Acad Radiol 25(12):1526–1532

    Article  PubMed  Google Scholar 

  22. Feragen A, Petersen J, Owen M, Lo P, Thomsen LH, Wille MMW, Dirksen A, Bruijne MD (2015) Geodesic atlas-based labeling of anatomical trees: application and evaluation on airways extracted from CT. IEEE Trans Med Imag 34(6):1212–1226

    Article  Google Scholar 

  23. Pinzón AM, Hoyos MH, Richard JC, Flórez-Valencia L, Orkisz M (2017) A tree-matching algorithm: application to airways in CT images of subjects with the acute respiratory distress syndrome. Med Image Anal 35:101–115

    Article  Google Scholar 

  24. Mori K, Shunsuke O, Deguchi D, Kitasaka T, Suenaga Y, Iwano S, Hasegawa Y, Takabatake H, Mori M, Natori H (2009) Automated anatomical labeling of bronchial branches extracted from CT datasets based on machine learning and combination optimization and its application to bronchoscope guidance. In: Medical image computing and computer assisted intervent(MICCAIs). Springer, Berlin, pp 707–714

  25. Feragen A, Petersen J, Owen M, Lo P, Thomsen L, Wille MMW, Dirksen A, Bruijne MD (2012) A hierarchical scheme for geodesic anatomical labeling of airway trees. In: Medical image computing and computer assisted intervent (MICCAI). Springer, New York, pp 147–155

  26. Palágyi K, Tschirren J, Hoffman EA, Sonka M (2006) Quantitative analysis of pulmonary airway tree structures. Comput Biol Med 36(9):974–996

    Article  PubMed  Google Scholar 

  27. Tschirren J, Palágyi K, Reinhardt JM, Hoffman EA, Sonka M (2002) Segmentation, skeletonization, and branchpoint matching - a fully automated quantitative evaluation of human intrathoracic airway trees. In Proc Miccai–5th Int Conf 2489:12–19

    Google Scholar 

  28. Schlathöelter T, Lorenz C, Carlsen IC, Renisch S, Deschamps T (2002) Simultaneous segmentation and tree reconstruction of the airways for virtual bronchoscopy. In Proc SPIE 4684:103–113

    Article  Google Scholar 

  29. Ginneken BV, Baggerman W, Rikxoort EV (2008) Robust segmentation and anatomical labeling of the airway tree from thoracic CT scans. In: Medical image computing and computer-assisted intervention–MICCAI, pp 219–226

  30. Pu J, Gu S, Liu S, Zhu S, Wilson D, Siegfried JM, Gur D (2012) CT Based computerized identification and analysis of human airways: a review. Med Phys 39(5):2603–2616

    Article  PubMed  PubMed Central  Google Scholar 

  31. Valencia LF, Pinzón AM, Richard JC, Hoyos MH, Orkisz M (2015) Simultaneous skeletonization and graph description of airway trees in 3D CT images. In Proc 25th Gretsi

  32. Saha PK, Borgefors G, di Baja GS (2016) A survey on skeletonization algorithms and their applications. Pattern Recogn Lett 76:3–12

    Article  Google Scholar 

  33. Delgado-Friedrichs O, Robins V, Sheppard A (2014) Skeletonization and partitioning of digital images using discrete morse theory. IEEE Trans Med Pattern Anal Mach Intell 37(3):654– 666

    Article  Google Scholar 

  34. Jin D, Iyer KS, Chen C, Hoffman EA, Saha PK (2016) A robust and efficient curve skeletonization algorithm for tree-like objects using minimum cost paths. Pattern Recogn Lett 76:32–40

    Article  Google Scholar 

  35. Aoki T, Murakami M, Koizumi T, Enami Y, Koike R, Fujimori A, Kusano T, Matsuda K, Yamada K, Nogaki K et al (2015) Skeletonization and isolation of the glissonean and venous branches in liver surgery with an ultrasonic scalpel technology. Int Surg 100(6):1048–1053

    Article  PubMed  PubMed Central  Google Scholar 

  36. Jalba AC, Kustra J, Telea AC (2012) Surface and curve skeletonization of large 3D models on the GPU. IEEE Trans Pattern Anal Mach Intell 35(6):1495–1508

    Article  Google Scholar 

  37. He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. arXiv:1512.03385

  38. Zeiler M D, Fergus R (2014) Visualizing and understanding convolutional networks. In: Fleet D., Pajdla T., Schiele B., Tuytelaars T. (eds) Computer vision ECCV 2014. ECCV 2014. Lecture notes in computer science, vol 8689. Springer, Cham

  39. Setio AAA, Traverso A, Bel TD, Berens MSN, Bogaard CVD, Cerello P, Chen H, Dou Q, Fantacci ME, Geurts B et al (2017) Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Med Image Anal 42:1–13

    Article  PubMed  Google Scholar 

  40. Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128

    Article  PubMed  Google Scholar 

  41. Black MJ, Sapiro G, Marimont DH, Heeger D (2002) Robust anisotropic diffusion. IEEE Trans Image Process 7(3):421–432

    Article  Google Scholar 

  42. Jin L (2017) Complex impulse noise removal from color images based on super pixel segmentation. J Vis Commun Image R 48:54–65

    Article  Google Scholar 

  43. Leader JK, Zheng B, Rogers RM, Sciurba FC, Gur D (2003) Automated lung segmentation in X-ray computed tomography: development and evaluation of a heuristic threshold-based scheme. Acad Radiol 10(11):1224–1236

    Article  PubMed  Google Scholar 

  44. He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proc IEEE Int Conf Comput Vis, pp. 1026–1034

  45. Baldi P (1995) Gradient descent learning algorithm overview: a general dynamical systems perspective. IEEE Trans Neural Networks 6(1):182–195

    Article  CAS  PubMed  Google Scholar 

  46. Sun Y, Wang X, Tang X (2013) Deep convolutional network cascade for facial point detection. In: Proc CVPR, pp. 3476– 3483

  47. Ge L, Ren Z, Li Y, Xue Z, Wang Y, Cai J, Yuan J (2019) 3D Hand Shape and Pose Estimation from a Single RGB Image. In: Proc CVPR, pp. 10833–10842

  48. Suwajanakorn S, Snavely N, Tompson J, Norouzi M (2018) Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning. In: Advances in neural information processing systems, pp. 2059–2070

  49. Pedersen JH, Ashraf H, Dirksen A, Bach K, Hansen H, Toennesen P, Mortensen J (2009) The Danish randomized lung cancer CT screening trial—overall design and results of the prevalence round. J Thorac Oncol 4(5):608–614

    Article  PubMed  Google Scholar 

Download references

Funding

This research is partially supported by the National Natural Science Foundation of China (Grant No. 81671768), National Key R & D Program of China (Grant No. 2017YFC0112804), and Fundamental Research Funds for the Central Universities of China, HUST (Grant No. 2016YXMS086).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China

    Manyang Wang, Renchao Jin, Hong Liu, Shan Jiang, Kang Li & XueXin Zhou

  2. Key Laboratory of Education Ministry for Image Processing and Intelligence Control, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China

    Manyang Wang, Renchao Jin, Hong Liu, Shan Jiang, Kang Li & XueXin Zhou

  3. Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China

    Nanchuan Jiang

  4. Hubei Province Key Laboratory of Molecular Imaging, Huazhong University of Science and Technology, Wuhan, 430022, China

    Nanchuan Jiang

Authors
  1. Manyang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Renchao Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nanchuan Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shan Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. XueXin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Renchao Jin.

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

Wang, M., Jin, R., Jiang, N. et al. Automated labeling of the airway tree in terms of lobes based on deep learning of bifurcation point detection. Med Biol Eng Comput 58, 2009–2024 (2020). https://doi.org/10.1007/s11517-020-02184-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-020-02184-y

Keywords

Search

Navigation