Automatic bronchial segmentation on ultra-HRCT scans: advantage of the 1024-matrix size with 0.25-mm slice thickness reconstruction



The aim of this study was to evaluate the advantages of ultra-high-resolution computed tomography (U-HRCT) for automatic bronchial segmentation.

Materials and methods

This retrospective study was approved by the Institutional Review Board, and written informed consent was waived. Thirty-three consecutive patients who underwent chest CT by a U-HRCT scanner were enrolled. In each patient, CT data were reconstructed by two different protocols: 512 × 512 matrix with 0.5-mm slice thickness (conventional HRCT mode) and 1024 × 1024 matrix with 0.25-mm slice thickness (U-HRCT mode). We used a research workstation to compare the two CT modes with regard to the numbers and total lengths of the automatically segmented bronchi.


Significantly greater numbers and longer lengths of peripheral bronchi were segmented in the U-HRCT mode than in the conventional HRCT mode (P < 0.001, for fifth- to eighth-generation bronchi). For example, the mean numbers and total lengths of the sixth-generation bronchi were 81 and 1048 mm in the U-HRCT mode and 59 and 538 mm in the conventional HRCT mode.


The U-HRCT mode greatly improves automatic airway segmentation for the more peripheral bronchi, compared with the conventional HRCT mode. This advantage can be applied to routine clinical care, such as virtual bronchoscopy and automatic lung segmentation.

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

Fig. 1
Fig. 2


  1. 1.

    Matsuoka S, Kurihara Y, Yagihashi K, Hoshino M, Nakajima Y. Airway dimensions at inspiratory and expiratory multisection CT in chronic obstructive pulmonary disease: correlation with airflow limitation. Radiology. 2008;248(3):1042–9.

    Article  Google Scholar 

  2. 2.

    Hasegawa M, Nasuhara Y, Onodera Y, Makita H, Nagai K, Fuke S, et al. Airflow limitation and airway dimensions in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2006;173:1309–15.

    Article  Google Scholar 

  3. 3.

    Yamashiro T, Matsuoka S, San Jose Estepar R, Dransfield MT, Diaz A, Reilly JJ, et al. Quantitative assessment of bronchial wall attenuation on thin-section CT: an indicator of airflow limitation in chronic obstructive pulmonary disease. Am J Roentgenol. 2010;195(2):363–9.

    Article  Google Scholar 

  4. 4.

    Hogg JC, Macklem PT, Thurlbeck WM. Site and nature of airway obstruction in chronic obstructive lung disease. N Engl J Med. 1968;278:1355–60.

    CAS  Article  Google Scholar 

  5. 5.

    Kakinuma R, Moriyama N, Muramatsu Y, Gomi S, Suzuki M, Nagasawa H, et al. Ultra-high-resolution computed tomography of the lung: image quality of a prototype scanner. PLoS ONE. 2015;10(9):e0137165.

    Article  Google Scholar 

  6. 6.

    Yanagawa M, Hata A, Honda O, Kikuchi N, Miyata T, Uranishi A, et al. Subjective and objective comparisons of image quality between ultra-high-resolution CT and conventional area detector CT in phantoms and cadaveric human lungs. Eur Radiol. 2018;28(12):5060–8.

    Article  Google Scholar 

  7. 7.

    Hata A, Yanagawa M, Honda O, Kikuchi N, Miyata T, Tsukagoshi S, et al. Effect of matrix size on the image quality of ultra-high-resolution CT of the lung: comparison of 512 × 512, 1024 × 1024, and 2048 × 2048. Acad Radiol. 2018;25(7):869–76.

    Article  Google Scholar 

  8. 8.

    Honda O, Yanagawa M, Hata A, Kikuchi N, Miyata T, Tsukagoshi S, et al. Influence of gantry rotation time and scan mode on image quality in ultra-high-resolution CT system. Eur J Radiol. 2018;103(1):71–5.

    Article  Google Scholar 

  9. 9.

    Yoshioka K, Tanaka R, Takagi H, Ueyama Y, Kikuchi K, Chiba T, et al. Ultra-high-resolution CT angiography of the artery of Adamkiewicz: a feasibility study. Neuroradiology. 2018;60(1):109–15.

    Article  Google Scholar 

  10. 10.

    Takagi H, Tanaka R, Nagata K, Ninomiya R, Arakita K, Schuijf JD, et al. Diagnostic performance of coronary CT angiography with ultra-high-resolution CT: comparison with invasive coronary angiography. Eur J Radiol. 2018;101:30–7.

    Article  Google Scholar 

  11. 11.

    Tanaka R, Yoshioka K, Takagi H, Schuijf JD, Arakita K. Novel developments in non-invasive imaging of peripheral arterial disease with CT: experience with state-of-the-art, ultra-high-resolution CT and subtraction imaging. Clin Radiol. 2019;74(1):51–8.

    CAS  Article  Google Scholar 

  12. 12.

    Nagata H, Murayama K, Suzuki S, Watanabe A, Hayakawa M, Saito Y, et al. Initial clinical experience of a prototype ultra-high-resolution CT for assessment of small intracranial arteries. Jpn J Radiol. 2019;37(4):283–91.

    Article  Google Scholar 

  13. 13.

    Xu Y, Yamashiro T, Moriya H, Muramatsu S, Murayama S. Quantitative emphysema measurement on ultra-high-resolution CT scans. Int J Chron Obstruct Pulmon Dis. 2019;14(1):2283–90.

    Article  Google Scholar 

  14. 14.

    Tanabe N, Oguma T, Sato S, Kubo T, Kozawa S, Shima H, et al. Quantitative measurement of airway dimensions using ultra-high resolution computed tomography. Respir Investig. 2018;56(6):489–96.

    Article  Google Scholar 

  15. 15.

    Tanabe N, Shima H, Sato S, Oguma T, Kubo T, Kozawa S, et al. Direct evaluation of peripheral airways using ultra-high-resolution CT in chronic obstructive pulmonary disease. Eur J Radiol. 2019;120:108687.

    Article  Google Scholar 

  16. 16.

    Matsunaga Y, Chida K, Kondo Y, Kobayashi K, Kobayashi M, Minami K, et al. Diagnostic reference levels and achievable doses for common computed tomography examinations: results from the Japanese nationwide dose survey. Br J Radiol. 2019;92(1094):20180290.

    Article  Google Scholar 

  17. 17.

    Higham A, Quinn AM, Cançado JED, Singh D. The pathology of small airways disease in COPD: historical aspects and future directions. Respir Res. 2019;20(1):49.

    Article  Google Scholar 

  18. 18.

    James AL, Wenzel S. Clinical relevance of airway remodelling in airway diseases. Eur Respir J. 2007;30(1):134–55.

    CAS  Article  Google Scholar 

  19. 19.

    Washko GR, Dransfield MT, Estépar RS, Diaz A, Matsuoka S, Yamashiro T, et al. Airway wall attenuation: a biomarker of airway disease in subjects with COPD. J Appl Physiol. 2009;107(1):185–91.

    Article  Google Scholar 

  20. 20.

    Horton KM, Horton MR, Fishman EK. Advanced visualization of airways with 64-MDCT: 3D mapping and virtual bronchoscopy. AJR Am J Roentgenol. 2007;189(6):1387–96.

    Article  Google Scholar 

  21. 21.

    Higgins WE, Ramaswamy K, Swift RD, McLennan G, Hoffman EA. Virtual bronchoscopy for three-dimensional pulmonary image assessment: state of the art and future needs. Radiographics. 1998;18(3):761–78.

    CAS  Article  Google Scholar 

  22. 22.

    Vining DJ, Liu K, Choplin RH, Haponik EF. Virtual bronchoscopy. Relationships of virtual reality endobronchial simulations to actual bronchoscopic findings. Chest. 1996;109:549–53.

    CAS  Article  Google Scholar 

  23. 23.

    Asano F, Eberhardt R, Herth FJ. Virtual bronchoscopic navigation for peripheral pulmonary lesions. Respiration. 2014;88(5):430–40.

    Article  Google Scholar 

  24. 24.

    Lee HY, Lee KS. Ground-glass opacity nodules: histopathology, imaging evaluation, and clinical implications. J Thorac Imaging. 2011;26(2):106–18.

    Article  Google Scholar 

  25. 25.

    Oda S, Awai K, Liu D, Nakaura T, Yanaga Y, Nomori H, et al. Ground-glass opacities on thin-section helical CT: differentiation between bronchioloalveolar carcinoma and atypical adenomatous hyperplasia. Am J Roentgenol. 2008;190(5):1363–8.

    Article  Google Scholar 

Download references


The authors thank the engineers and technologists of Ziosoft for their excellent help in developing the research software.


University of the Ryukyus receives a research grant from Canon Medical Systems and Ziosoft.

Author information



Corresponding author

Correspondence to Yuka Morita.

Additional information

Publisher's Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 572 kb)

About this article

Verify currency and authenticity via CrossMark

Cite this article

Morita, Y., Yamashiro, T., Tsuchiya, N. et al. Automatic bronchial segmentation on ultra-HRCT scans: advantage of the 1024-matrix size with 0.25-mm slice thickness reconstruction. Jpn J Radiol (2020).

Download citation


  • Ultra-high-resolution computed tomography
  • Bronchus
  • Airway segmentation
  • Quantitative measurement