Top

International Journal of Computer Assisted Radiology and Surgery

Published in:

01-11-2017 | Original Article

A pilot study for segmentation of pharyngeal and sino-nasal airway subregions by automatic contour initialization

Authors: Bala Chakravarthy Neelapu, Om Prakash Kharbanda, Viren Sardana, Abhishek Gupta, Srikanth Vasamsetti, Rajiv Balachandran, Shailendra Singh Rana, Harish Kumar Sardana

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 11/2017

Abstract

Purpose

The objective of the present study is to put forward a novel automatic segmentation algorithm to segment pharyngeal and sino-nasal airway subregions on 3D CBCT imaging datasets.

Methods

A fully automatic segmentation of sino-nasal and pharyngeal airway subregions was implemented in MATLAB programing environment. The novelty of the algorithm is automatic initialization of contours in upper airway subregions. The algorithm is based on boundary definitions of the human anatomy along with shape constraints with an automatic initialization of contours to develop a complete algorithm which has a potential to enhance utility at clinical level. Post-initialization; five segmentation techniques: Chan-Vese level set (CVL), localized Chan-Vese level set (LCVL), Bhattacharya distance level set (BDL), Grow Cut (GC), and Sparse Field method (SFM) were used to test the robustness of automatic initialization.

Results

Precision and F-score were found to be greater than 80% for all the regions with all five segmentation methods. High precision and low recall were observed with BDL and GC techniques indicating an under segmentation. Low precision and high recall values were observed with CVL and SFM methods indicating an over segmentation. A Larger F-score value was observed with SFM method for all the subregions. Minimum F-score value was observed for naso-ethmoidal and sphenoidal air sinus region, whereas a maximum F-score was observed in maxillary air sinuses region. The contour initialization was more accurate for maxillary air sinuses region in comparison with sphenoidal and naso-ethmoid regions.

Conclusion

The overall F-score was found to be greater than 80% for all the airway subregions using five segmentation techniques, indicating accurate contour initialization. Robustness of the algorithm needs to be further tested on severely deformed cases and on cases with different races and ethnicity for it to have global acceptance in Katradental radKatraiology workflow.

Cossellu G, Biagi R, Sarcina M, Mortellaro C, Farronato G (2015) Three-dimensional evaluation of upper airway in patients with obstructive sleep apnea syndrome during oral appliance therapy. J Craniofac Surg 26(3):745–748. doi:10.1097/scs.0000000000001538 CrossRefPubMed

Cui DM, Han DM, Nicolas B, Hu CL, Wu J, Su MM (2016) Three-dimensional evaluation of nasal surgery in patients with obstructive sleep apnea. Chin Med J 129(6):651–656. doi:10.4103/0366-6999.177971 CrossRefPubMedPubMedCentral

Neugebauer J, Ritter L, Mischkowski RA, Dreiseidler T, Scherer P, Ketterle M, Rothamel D, Zoller JE (2010) Evaluation of maxillary sinus anatomy by cone-beam CT prior to sinus floor elevation. Int J Oral Maxillofac Implants 25(2):258–265PubMed

Sam K, Lam B, Ooi CG, Cooke M, Ip MS (2006) Effect of a non-adjustable oral appliance on upper airway morphology in obstructive sleep apnoea. Respir Med 100(5):897–902. doi:10.1016/j.rmed.2005.08.019 CrossRefPubMed

Sforza E, Bacon W, Weiss T, Thibault A, Petiau C, Krieger J (2000) Upper airway collapsibility and cephalometric variables in patients with obstructive sleep apnea. Am J Respir Crit Care Med 161(2):347–352. doi:10.1164/ajrccm.161.2.9810091 CrossRefPubMed

Shepard JW Jr, Burger CD (1990) Nasal and oral flow-volume loops in normal subjects and patients with obstructive sleep apnea. Am Rev Respir Dis 142(6 Pt 1):1288–1293. doi:10.1164/ajrccm/142.6_Pt_1.1288 CrossRefPubMed

Souza FJ, Evangelista AR, Silva JV, Perico GV, Madeira K (2016) Cervical computed tomography in patients with obstructive sleep apnea: influence of head elevation on the assessment of upper airway volume. J Brasil Pneumol 42(1):55–60. doi:10.1590/s1806-37562016000000092 CrossRef

Wang T, Yang Z, Yang F, Zhang M, Zhao J, Chen J, Li Y (2014) A three dimensional study of upper airway in adult skeletal Class II patients with different vertical growth patterns. PLoS ONE 9(4):e95544. doi:10.1371/journal.pone.0095544 CrossRefPubMedPubMedCentral

White SM, Huang CJ, Huang SC, Sun Z, Eldredge JD, Mallya SM (2015) Evaluation of the upper airway morphology: the role of cone beam computed tomography. J Calif Dental Assoc 43(9):531–539

10.

Eslami E, Katz ES, Baghdady M, Abramovitch K, Masoud MI (2016) Are three-dimensional airway evaluations obtained through computed and cone-beam computed tomography scans predictable from lateral cephalograms? A systematic review of evidence. The Angle Orthod. doi:10.2319/032516-243.1

11.

Alsufyani NA, Hess A, Noga M, Ray N, Al-Saleh MA, Lagravere MO, Major PW (2016) New algorithm for semiautomatic segmentation of nasal cavity and pharyngeal airway in comparison with manual segmentation using cone-beam computed tomography. Am J Orthod Dentofac Orthop 150(4):703–712. doi:10.1016/j.ajodo.2016.06.024 CrossRef

12.

Dastidar P, Heinonen T, Numminen J, Rautiainen M, Laasonen E (1999) Semi-automatic segmentation of computed tomographic images in volumetric estimation of nasal airway. European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS): affiliated with the German Society for Oto-Rhino-Laryngology. Head Neck Surg 256(4):192–198

13.

Salerno S, Gagliardo C, Vitabile S, Militello C, La Tona G, Giuffre M, Lo Casto A, Midiri M (2014) Semi-automatic volumetric segmentation of the upper airways in patients with pierre robin sequence. Neuroradiol J 27(4):487–494. doi:10.15274/nrj-2014-10067 CrossRefPubMedPubMedCentral

14.

Weissheimer A, Menezes LM, Sameshima GT, Enciso R, Pham J, Grauer D (2012) Imaging software accuracy for 3-dimensional analysis of the upper airway. Am J Orthod Dentofac Orthop 142(6):801–813. doi:10.1016/j.ajodo.2012.07.015 CrossRef

15.

Tingelhoff K, Moral AI, Kunkel ME, Rilk M, Wagner I, Eichhorn KG, Wahl FM, Bootz F (2007) Comparison between manual and semi-automatic segmentation of nasal cavity and paranasal sinuses from CT images. In: Conference proceedings: annual international conference of the IEEE engineering in medicine and biology society IEEE engineering in medicine and biology society annual conference 2007, pp 5505–5508. doi:10.1109/iembs.2007.4353592

16.

Huang R, Li A, Bi L, Li C, Young P, King G, Feng DD (2016) Kim J A locally constrained statistical shape model for robust nasal cavity segmentation in computed tomography. In: 2016 IEEE 13th International symposium on biomedical imaging (ISBI), 13–16 April 2016, pp 1334–1337. doi:10.1109/ISBI.2016.7493513

17.

Jinda-apiraksa A, Ongt SH, Hiew LT, Foong KWC, Kondo T (2009) A segmentation technique for maxillary sinus using the 3-D level set method. In: TENCON-2009 IEEE region 10 conference, 23–26 Jan. 2009, 2009, pp 1–6. doi:10.1109/TENCON.2009.5396044

18.

Bui NL, Ong SH, Foong KW (2015) Automatic segmentation of the nasal cavity and paranasal sinuses from cone-beam CT images. Int J Comput Assist Radiol Surg 10(8):1269–1277. doi:10.1007/s11548-014-1134-5 CrossRefPubMed

19.

Last C, Winkelbach S, Wahl FM, Eichhorn KWG, Bootz F (2010) A model-based approach to the segmentation of nasal cavity and paranasal sinus boundaries. In: Goesele M, Roth S, Kuijper A, Schiele B, Schindler K (eds) Pattern recognition: 32nd DAGM symposium, Darmstadt, Germany, September 22–24, 2010. Proceedings. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 333–342. doi:10.1007/978-3-642-15986-2_34

20.

Shi H, Scarfe WC, Farman AG (2006) Upper airway segmentation and dimensions estimation from cone-beam CT image datasets. Int J Comput Assist Radiol Surg 1(3):177–186. doi:10.1007/s11548-006-0050-8 CrossRef

21.

Cheng I, Nilufar S, Flores-Mir C, Basu A (2007) Airway segmentation and measurement in CT images. In: Conference proceedings: annual international conference of the IEEE engineering in medicine and biology society IEEE engineering in medicine and biology society annual conference 2007, pp 795–799. doi:10.1109/iembs.2007.4352410

22.

Shi H, Scarfe WC, Farman AG (2006) Maxillary sinus 3D segmentation and reconstruction from cone beam CT data sets. Int J Comput Assist Radiol Surg 1(2):83–89. doi:10.1007/s11548-006-0041-9

23.

Lankton S, Tannenbaum A (2008) Localizing region-based active contours. IEEE Trans Image Process 17(11):2029–2039. doi:10.1109/TIP.2008.2004611 CrossRefPubMedPubMedCentral

24.

Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277. doi:10.1109/83.902291 CrossRefPubMed

25.

Caselles V, Kimmel R, Sapiro G (1997) Geodesic active contours. Int J Comput Vis 22(1):61–79. doi:10.1023/a:1007979827043 CrossRef

26.

Vezhnevets V, Konouchine V GrowCut (2005) Interactive multi-label ND image segmentation by cellular automata. In: Proceedings of the graphicon, pp 150–156

27.

Lankton S (2009) Sparse field methods-Technical report. Georgia Institute of Technology

28.

Gupta A, Kharbanda OP, Balachandran R, Sardana V, Kalra S, Chaurasia S, Sardana HK (2017) Precision of manual landmark identification between as-received and oriented volume-rendered cone-beam computed tomography images. Am J Orthod Dentofac Orthop 151(1):118–131. doi:10.1016/j.ajodo.2016.06.027 CrossRef

29.

Smith T, Ghoneima A, Stewart K, Liu S, Eckert G, Halum S, Kula K (2012) Three-dimensional computed tomography analysis of airway volume changes after rapid maxillary expansion. Am J Orthod Dentofac Orthop 141(5):618–626. doi:10.1016/j.ajodo.2011.12.017 CrossRef

30.

Guijarro-Martinez R, Swennen GR (2013) Three-dimensional cone beam computed tomography definition of the anatomical subregions of the upper airway: a validation study. Int J Oral Maxillofac Surg 42(9):1140–1149. doi:10.1016/j.ijom.2013.03.007 CrossRefPubMed

31.

Vasamsetti S, Sardana V, Kumar P, Kharbanda O, Sardana H (2015) Automatic landmark identification in lateral cephalometric images using optimized template matching. J Med Imaging Health Inform 5(3):458–470CrossRef

32.

Loy G, Eklundh J-O (2006) Detecting symmetry and symmetric constellations of features. In: Leonardis A, Bischof H, Pinz A (eds) Computer vision—ECCV 2006: 9th European conference on computer vision, Graz, Austria, May 7–13, 2006. Proceedings, Part II. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 508–521. doi:10.1007/11744047_39

33.

Abdolali F, Zoroofi RA, Otake Y, Sato Y (2016) Automatic segmentation of maxillofacial cysts in cone beam CT images. Comput Biol Med 72:108–119. doi:10.1016/j.compbiomed.2016.03.014 CrossRefPubMed

Title: A pilot study for segmentation of pharyngeal and sino-nasal airway subregions by automatic contour initialization
Authors: Bala Chakravarthy Neelapu
Om Prakash Kharbanda
Viren Sardana
Abhishek Gupta
Srikanth Vasamsetti
Rajiv Balachandran
Shailendra Singh Rana
Harish Kumar Sardana
Publication date: 01-11-2017
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 11/2017
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-017-1650-1

At a glance: The STEP trials

Springer Medicine

A pilot study for segmentation of pharyngeal and sino-nasal airway subregions by automatic contour initialization

Abstract

Purpose

Methods

Results

Conclusion

At a glance: The STEP trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 11/2017

Development of angle information system to facilitate the adjustment of needle-holding posture

Measurement of smaller colon polyp in CT colonography images using morphological image processing

Automatic seed picking for brachytherapy postimplant validation with 3D CT images

Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studies

Trans-oral miniature X-ray radiation delivery system with endoscopic optical feedback

Validation workflow for a clinical Bayesian network model in multidisciplinary decision making in head and neck oncology treatment