Segmentation of distal airways using structural analysis

Autoři: Debora Gil aff001;  Carles Sanchez aff001;  Agnes Borras aff001;  Marta Diez-Ferrer aff002;  Antoni Rosell aff003
Působiště autorů: Comp. Vision Center and Comp. Science Dept, UAB, Barcelona, Spain aff001;  Pneumology Unit, Hosp. Univ. Bellvitge, IDIBELL, CIBERES, Barcelona, Spain aff002;  Hosp. Univ. Germans Trias i Pujol, Badalona, Spain aff003
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article


Segmentation of airways in Computed Tomography (CT) scans is a must for accurate support of diagnosis and intervention of many pulmonary disorders. In particular, lung cancer diagnosis would benefit from segmentations reaching most distal airways. We present a method that combines descriptors of bronchi local appearance and graph global structural analysis to fine-tune thresholds on the descriptors adapted for each bronchial level. We have compared our method to the top performers of the EXACT09 challenge and to a commercial software for biopsy planning evaluated in an own-collected data-base of high resolution CT scans acquired under different breathing conditions. Results on EXACT09 data show that our method provides a high leakage reduction with minimum loss in airway detection. Results on our data-base show the reliability across varying breathing conditions and a competitive performance for biopsy planning compared to a commercial solution.

Klíčová slova:

Algorithms – Anisotropy – Bronchi – Computed axial tomography – Convolution – Diagnostic medicine – Pulmonary imaging – Structural analysis


1. Osborne D, Vock P, Godwin JD, Silverman PM. CT identification of bronchopulmonary segments: 50 normal subjects. American journal of roentgenology. 1984;142(1):47–52. doi: 10.2214/ajr.142.1.47 6606964

2. Dolina MY, Cornish DC, et al. Interbronchoscopist variability in endobronchial path selection: a simulation study. CHEST. 2008;133 (4):897–905. doi: 10.1378/chest.07-2540 18263679

3. Reynisson PJ, Leira HO, Hernes TN, Hofstad EF, Scali M, Sorger H, et al. Navigated bronchoscopy: a technical review. Journal of bronchology & interventional pulmonology. 2014;21(3):242–264. doi: 10.1097/LBR.0000000000000064

4. Eberhardt R, Kahn N, Gompelmann D, Schumann M, Heussel CP, Herth FJF. LungPoint–a new approach to peripheral lesions. JThorac Oncol. 2010;5(10):1559–1563. doi: 10.1097/JTO.0b013e3181e8b308

5. Pu J, Gu S, Liu S, Zhu S, Wilson D, Siegfried JM, et al. CT based computerized identification and analysis of human airways: a review. Medical physics. 2012;39(5):2603–2616. doi: 10.1118/1.4703901 22559631

6. Diez-Ferrer M, Cubero N, Lopez R, Minchole E, Dorca J, Rosell A. Relation between the bronchus sign and segmented airways in virtual bronchoscopic navigation. Impact on bronchoscopic diagnostic yield. In: ECBIP; 2017.

7. Nardelli P, Khan KA, Corvò A, Moore N, Murphy MJ, Twomey M, et al. Optimizing parameters of an open-source airway segmentation algorithm using different CT images. Biomedical engineering online. 2015;14(1):62. doi: 10.1186/s12938-015-0060-2 26112975

8. Reynisson PJ, Scali M, Smistad E, Hofstad EF, Leira HO, Lindseth F, et al. Airway Segmentation and Centerline Extraction from Thoracic CT–Comparison of a New Method to State of the Art Commercialized Methods. PloS one. 2015;10(12):e0144282. doi: 10.1371/journal.pone.0144282 26657513

9. Xu Z, Bagci U, Foster B, Mollura DJ. A hybrid multi-scale approach to automatic airway tree segmentation from CT scans. In: ISBI; 2013.

10. Rizi FY, Ahmadian A, Rezaie N, Iranmanesh SA. Leakage suppression in human airway tree segmentation using shape optimization based on fuzzy connectivity method. Int J Imaging Syst Technol. 2013;23(1):71–84. doi: 10.1002/ima.22040

11. Salito C, Barazzetti L, Woods JC, Aliverti A. 3D airway tree reconstruction in healthy subjects and emphysema. Lung. 2011;189(4):287–293. doi: 10.1007/s00408-011-9305-4

12. Gao D, Gao X, Ni C, Zhang T. MGRG-morphological gradient based 3D region growing algorithm for airway tree segmentation in image guided intervention therapy. In: ISBB; 2011. p. 76–79.

13. Graham MW, Gibbs JD, Cornish DC, Higgins WE. Robust 3-D airway tree segmentation for image-guided peripheral bronchoscopy. IEEE transactions on medical imaging. 2010;29(4):982–997. doi: 10.1109/TMI.2009.2035813 20335095

14. Petersen J, Nielsen M, Lo P, Saghir Z, Dirksen A, De Bruijne M. Optimal graph based segmentation using flow lines with application to airway wall segmentation. In: BICIPMI; 2011. p. 49–60.

15. Bauer C, Eberlein M, Beichel RR. Graph-based airway tree reconstruction from chest CT scans: evaluation of different features on five cohorts. TMI. 2015;34 (5):1063–1076.

16. Charbonnier JP, Van Rikxoort EM, Setio AA, Schaefer-Prokop CM, van Ginneken B, Ciompi F. Improving airway segmentation in computed tomography using leak detection with convolutional networks. Medical image analysis. 2017;36:52–60. doi: 10.1016/ 27842236

17. Lo P, van Ginneken B, Reinhardt JM, Yavarna T, de Jong PA, Irving B, et al. Extraction of Airways from CT (EXACT09). TMI. 2012;31:2093–2107.

18. Diez-Ferrer M, Gil D, Carreño E, Padrones S, Aso S, Vicens V, et al. Positive airway pressure-enhanced CT to improve virtual bronchoscopic navigation. In: AABIP-CHEST; 2016.

19. Burden R, Faires J. Numerical Analysis (3rd ed). PWS Publishers; 1985.

20. Lowe D. Distinctive Image Features from Scale-Invariant Keypoints. IJCV. 2004;60(2):91–110. doi: 10.1023/B:VISI.0000029664.99615.94

21. Bertalmío M, Sapiro G, Caselles V, Ballester C. Image Inpainting. In: SIGGRAPH; 2000.

22. Chatfield K, Simonyan K, Vedaldi A, Zisserman A. Return of the Devil in the Details: Delving Deep into Convolutional Nets. In: BMVC; 2014.

23. Van Uitert R, Bitter I. Subvoxel precise skeletons of volumetric data based on fast marching methods. Med Phys. 2007;34(2):627–638. doi: 10.1118/1.2409238 17388180

24. Kerschnitzki M, Kollmannsberger P, Burghammer M, Duda GN, Weinkamer R, Wagermaier W, et al. Architecture of the osteocyte network correlates with bone material quality. JBMR. 2013;28(8):1837–45. doi: 10.1002/jbmr.1927

25. Diez-Ferrer M, Gil D, Tebe C, Sanchez C, Cubero N, López-Lisbona R, et al. Positive airway pressure to enhance computed tomography imaging for airway segmentation for virtual bronchoscopic navigation. Respiration. 2018;96(6):525–534. doi: 10.1159/000490915 30227414

26. Stoel BC, Putter H, Bakker ME, Dirksen A, Stockley RA, Piitulainen E, et al. Volume correction in computed tomography densitometry for follow-up studies on pulmonary emphysema. Proceedings of the American Thoracic Society. 2008;5(9):919–924. doi: 10.1513/pats.200804-040QC 19056717

27. F Inoue YLWI Y Kitamura. Robust airway extraction based on machine learning and minimum spanning tree. In: SPIE; 2013.

28. Ramírez E, Sánchez C, Borràs A, Diez-Ferrer M, Rosell A, Gil D. BronchoX: bronchoscopy exploration software for biopsy intervention planning. Healthcare technology letters. 2018;5(5):177–182. doi: 10.1049/htl.2018.5074 30464850

Článek vyšel v časopise


2019 Číslo 12
Nejčtenější tento týden