Reliability and accuracy of a semi-automatic segmentation protocol of the nasal cavity using cone beam computed tomography in patients with sleep apnea

doi:10.21203/rs.3.rs-2449139/v1

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Reliability and accuracy of a semi-automatic segmentation protocol of the nasal cavity using cone beam computed tomography in patients with sleep apnea

https://doi.org/10.21203/rs.3.rs-2449139/v1

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published 05 Oct, 2023

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Objectives: The objectives of this study included using the cone beam computed tomography (CBCT) technology to assess: (1) intra- and inter-observer reliability of the volume measurement of the nasal cavity; (2) the accuracy of the segmentation protocol for evaluation of the nasal cavity.

Materials and Methods: This study used test-retest reliability and accuracy methods within two different population sample groups, from Eastern Asia and North America. Thirty obstructive sleep apnea (OSA) patients were randomly selected from administrative and research oral health data archived at two dental faculties in China and Canada. To assess the reliability of the protocol, two observers performed nasal cavity volume measurement twice with a 10-day interval, using Amira software (v4.1, Visage Imaging Inc., Carlsbad, CA).

The accuracy study used a computerized tomography (CT) scan of an OSA patient, who was not included in the study sample, to fabricate an anthropomorphic phantom of the nasal cavity volume with known dimensions (gold standard). This phantom was scanned using one NewTom 5G (QR systems, Verona, Italy) CBCT scanner. The nasal cavity was segmented based on CBCT images and converted into standard tessellation language (STL) models. The volume of the nasal cavity was measured on the acquired STL models.

Results: The intra-observer and inter-observer intraclass correlation coefficients for the volume measurement of the nasal cavity were 0.980-0.997 and 0.948-0.992 consecutively. The nasal cavity volume measurement was overestimated by 1.1%-3.1%, compared to the gold standard.

Conclusions: The semi-automatic segmentation protocol of the nasal cavity in patients with sleep apnea and by using cone beam computed tomography is reliable and accurate.

Clinical Relevance: This study provides a reliable and accurate protocol for segmentation of nasal cavity, which will facilitate the clinician to analyze the images within nasoethmoidal region.

cone beam computed tomography

nasal cavity

reliability

accuracy

obstructive sleep apnea

The nose is an important human organ with multiple functions, including olfaction, filtering, humidifying inhaled air (1). Analysis of nasal cavity anatomy can help detect certain disorders, such as obstructive sleep apnea (OSA) (2, 3). Evidence from population studies shows the role of nasal obstruction in the pathophysiology of OSA (4, 5) Often, the treatment plan for patients who suffer from OSA and have chronic nasal obstruction could be nasal surgery, such as septoplasty (6, 7). Thus, it is recommended that patients with OSA undergo further nasal analysis as an aid in diagnostics and in treatment decision making (8).

The anatomy of the nasal cavity is very complex and varies among different races and ethnic groups (9). To the best of our knowledge, there are a limited number of studies using 3D analysis to analyze the anatomy of the nasal cavity (2, 9, 10). Furthermore, these studies mainly used computerized tomography (CT) images, which expose patients to a high radiation dose. More recently, cone beam computed tomography (CBCT) has been used as an alternative tool in the diagnostics of diseases and anatomic variations with a lower radiation exposure risk for patients (11, 12). However, studies using CBCT for the 3D analysis of the nasal cavity in OSA patients of various ethnicities, are still very limited (13). In this study, we develop a semi-automatic segmentation protocol for 3D analysis of the nasal cavity in OSA patients. It will be interesting to investigate whether our protocol for 3D analysis of the nasal cavity is reliable for different ethnic groups.

The accuracy of the 3D analysis of the nasal cavity is another important issue. To test accuracy, it is necessary to create a phantom of the nasal cavity to serve as a “gold standard”. Recent developments in the field of three-dimensional (3D) printing offer new opportunities for manufacturing lifelike anthropomorphic phantoms (14), allowing us to use them as a gold standard.

Therefore, this study aimed to test the reliability and accuracy of a semi-automatic segmentation protocol for the 3D analysis of the nasal cavity.

1. Reliability of nasal cavity measurement

Data source

Date source consisted of CBCT images of patients with diagnosed sleep apnea from two different countries. This included CBCT images of 15 Chinese and 15 Canadian patients with OSA which were randomly selected from scans available at the Department of Oral Radiology, Shandong University, China and the Department of Oral Radiology, University of Montreal, Canada. These images were obtained during previous studies (15) and IRB approvals were obtained.

The inclusion criteria were as follows: age >18 years and availability of CBCT images covering the entire nasal cavity from the nasion point to the level of the hard palate. The exclusion criteria were presence of a palatal cleft, presence of a craniofacial syndrome, and craniofacial surgery in the past.

The CBCT images were obtained using NewTom 5G (QR systems, Verona, Italy) machines in both oral radiology departments, according to the standard imaging protocol. During the CBCT, patients were positioned in the supine position, with the Frankfort horizontal (FH) plane perpendicular to the floor. They were instructed to maintain maximum intercuspation and to avoid swallowing and other movements during the scanning period. The exposure settings were 110 kV, 4 mA, 18*16 cm field of view, 0.3 mm voxel size, 18 to 36 seconds scanning time, depending on the size of the patient. For further analysis, the images were saved as Digital Imaging and Communications in Medicine (DICOM) files, and these data sets were imported into Amira software (v4.1, Visage Imaging Inc., Carlsbad, CA) for nasal cavity measurements.

Measurement and semi-automated nasal cavity segmentation protocol

Using imaging data sets not related to this study, two orthodontists were calibrated and trained as observers. After calibration, each observer performed the nasal cavity measurements on all images twice within a 10-day interval. The observers were blinded to patients’ information during the measurements.

The Amira software was used for the semi-automatic segmentation of nasal cavity as follows: first, a voxel set was built to include all nasal cavity information. second, a new mask was built with its thresholds ranging from -1000 to -300. third, the boundaries of the nasal cavity were selected in the corresponding axial plans and put into the voxel set. The boundaries were set as follows (Fig 1): the upper boundary (i.e., the plane across the anterior point of the sphenoid parallel to the FH plane); the lower boundary (i.e., the last axial slice to show the nasal cavity); the left and right boundaries (i.e., the sagittal plane across the mesial point of the maxillary sinus); the anterior boundary (i.e., the nostril not connected with the outside); the posterior boundary (i.e., the coronal plane across posterior nasal spine point). Fourth, all of the slices within the above boundaries were selected and put into the voxel set. Finally, all slices were checked to make sure no area was missing and, if necessary, modified properly using the tools integrated in the software. The software then calculated the total volume of the nasal cavity.

2. Accuracy of nasal cavity measurement

A CT data set (Discovery CT 750, General Electric Healthcare, Milwaukee, USA) of a 40-year-old Chinese male previously was used to design an anthropomorphic phantom of the nasal cavity. The aforementioned CT data set was converted into a virtual 3D surface, i.e., a standard tessellation language (STL) model of the nasal cavity. The STL model of the nasal cavity was subsequently used to build the phantom. It was then printed using polylactic acid (PLA) and a 3D printer (Aurora, Shenzhen, China) (Fig 2a). Then an impression of the PLA-phantom was acquired using liquid silicone (Wacker, Germany) (Fig 2b). After taking the PLA-phantom out of the silicone, the phantom of the nasal cavity was obtained.

The next step was to perform a CBCT scan (NewTom 5G, Verona, Italy) on the silicone phantom. The acquired CBCT data set was saved as DICOM files and was imported into Amira® software (v4.1, Visage Imaging Inc., Carlsbad, California, USA) (Fig 2c). The segmentation procedure on the DICOM data sets of the nasal cavity was performed five times by the two calibrated orthodontists and was subsequently repeated after a 10-day interval following the procedure as follows: First, a voxel set was built to include all of the nasal cavity information; second, a new mask was built with its thresholds ranging from -1000 to -300; finally, the entire slice was scrolled to check if any area was missing, and corrections were applied, if needed, using the tools integrated in the software. This resulted in a total of 20 values for nasal cavity volume (experimental nasal cavity measurements). The volume of the PLA-phantom was used as our gold standard value.

Statistical methods

Sample size:

The power calculation recommended by Walter et al. for reliability studies was followed (16). The null hypothesis was defined as H0: ρ0 ≦ 0.6, and the alternative hypothesis was defined as H1: ρ1≧0.8. The rate of type I error (a), which equates to the criterion for significance, was set at 0.05. The rate of type II error (b), which is related to the power of a test (1-b), was set at 0.2. After reviewing Table II in Walter et al.’s study, the proposed sample size was set at 15 patients (17).

Intraclass correlation coefficients (ICCs) were calculated to determine the intra- and inter-observer reliability of the nasal cavity volume measurements. Reliability was divided into three categories: poor (ICC < 0.40), fair to good (0.40 to ≦0.75), and excellent (ICC > 0.75).

The accuracy of the protocol was calculated as the ratio of the experimental nasal cavity measurements to the gold standard in percent. To evaluate the accuracy of the protocol, a one-sample t-test was used to test the difference between the experimental nasal cavity measurements and the gold standard value. The measurement error (%) was calculated as the difference between the experimental nasal cavity measurements and the gold standard value. Data were analyzed using the Statistical Package for Social Sciences for Windows (version 21, SPSS Inc., Chicago, IL). Statistical significance was established at α=0.05.

Reliability

Table 1 presents the nasal cavity measurements and the ICCs of intra- and inter-observer reliability. Both intra- and inter-observer reliability of the nasal cavity measurements were excellent (ICC=0.94-1.00).

Table 1. Intra-observer and inter-observer reliability of the measurement of the nasal cavity volume, estimated by intraclass correlation coefficients (ICCs).

Volume	Intra-observer Mean±SD (ICC)*		Inter-observer Mean±SD (ICC)
	Observer 1	Observer 2
Chinese OSA patients	15.18±4.79 (0.989)	14.89±4.82(0.994)	15.03±4.76 (0.992)
Caucasian OSA patients	12.16±3.04 (0.980)	11.59±3.14 (0.997)	11.87±3.08 (0.948)

*ICC = intraclass correlation coefficients

Accuracy

The volume measurement

The 3D printed anthropomorphic phantom and the CBCT images of this phantom are shown in Fig 2. A statistically significant difference (p < 0.05) was found between the gold standard and the measurement of the nasal cavity (Mean±SD: 18.99±0.066). The measurements of the volume of the nasal cavity were larger than those of the gold standard. The measurement error in volume was 1.1%-3.1%. Bland–Altman plots showed the agreement between observers during the accuracy measurement (Fig 3).

In our study, both intra-observer reliability and inter-observer reliability were excellent for the nasal cavity measurement on CBCT images of OSA patients. The nasal cavity volume measurement methodology used in this study can be applied in future research in a reliable way. A phantom of a nasal cavity was used in this study to test the accuracy of the measurements. Our results showed that 3D measurement, such as the volume of the nasal cavity, obtained from CBCT images gives an accurate representation of the anatomic dimension of the phantom.

Nasal cavity

CBCT is playing an increasingly important role in the diagnosis of morphologic abnormalities in the oral and maxillofacial region (18). Clinician must also be able to interpret the anatomical regions outside the dentomaxillary complex, such as the nasoethmoidal region (19). The nose is a complex and important structure in facial aesthetics and respiratory physiology (1). Besides, the nose plays an important role in the physiology of sleep by regulating nasal airway resistance and stimulating ventilation (20). Nasal obstruction is common in sleep apnea, contributes to OSA, and interferes with tolerance of OSA treatment with continuous positive airway pressure (CPAP) or oral appliances (20, 21). Moreover, the anatomy of the nasal cavity varies among different ethnic groups, such as Chinese versus Caucasian (9). Therefore, it is necessary to develop a protocol for nasal cavity measurement, which could help clinicians diagnose and develop a treatment plan and which takes into consideration the characteristics of different ethnic groups.

Reliability

In previous studies, several segmentation procedures were developed to segment the nasal cavity with paranasal sinuses based on CBCT images (13, 22). This can be a very time-consuming task when one chooses to exclude the sinuses from the simulation domain (23). To facilitate computational fluid dynamics (CFD) analysis of the nasal cavity, Keustermans et al., developed a method to segment the nasal cavity based on active shape modelling (23). However, this method is difficult for clinicians. In our study, we developed a protocol for segmentation of the nasal cavity suitable for use by clinicians. In this protocol, the boundary was defined using anatomical landmarks with high reliability, such as the posterior nasal spine point (17). In this way, the clinician was able to segment the nasal cavity without knowing the complex algorithm integrated in the software.

Although the observers in our study had different experiences with the use of the software, they showed good inter-observer reliabilities (ICC = 0.94-0.992) in the volume measurement of the nasal cavity. This finding can be explained by several factors. First, the landmark localization was well defined, and the observers were trained before performing the measurements. Second, a fixed threshold ranging from -1000 to -300 was used for the selection of the nasal cavity. Although the observers had to scroll through all the slices to check if any area was missing and make modifications when needed using the tools integrated in the software, reliability was still high. Observer’s subjectivity in nasal cavity segmentation could be eliminated. Therefore, it is recommended to use semi-automatic segmentation of the region of interest in studies assessing the characteristics of the nasal cavity to improve the reliability of the measurements.

Accuracy

To test accuracy, a phantom with known dimensions is often used as gold standard. In our pilot study, different approaches were tried to obtain such a phantom. At the beginning, a skull model was used as the phantom. However, since the nasal cavity is partially enclosed by soft tissue, and there is no soft tissue in the skull model, the complexity of the nasal cavity cannot be represented by the skull model. Nowadays, 3D printing applications are expanding. Using this technique, the nasal cavity and its surrounding tissues can be segmented and directly printed by a 3D printer. As the anatomy of the nasal cavity is very complex, during 3D printing, supporters have to be added to the phantom to hold the nasal cavity walls. This does not resemble the true structure of the nasal cavity. To address this problem, in this study, after removing the supports from the PLA-phantom, we took an impression of the PLA-phantom using liquid silicone. Therefore, the silicone phantom used in the present study offered the unique possibility of assessing the accuracy of nasal cavity measurements under real, lifelike conditions (24).

In this study, nasal cavity volume was overestimated by 1.1%-3.1%. This phenomenon can be explained by the CBCT image acquisition process and/or the subsequent image segmentation by means of thresholding. During CBCT image acquisition, anatomical structures are differentiated based on their radiographic density. However, voxels residing on tissue boundaries can represent more than one tissue type. This phenomenon is known as the partial volume effect. The result of the partial volume effect is that voxels could erroneously be allocated to “nasal cavity” instead of “soft tissue” during the image segmentation process (25). Another reason is that as the anatomy of nasal cavity is very complex, the observer’s experience in assessment of the nasal cavity could influence the results. Therefore, it is advisable to train the observers before assessment.

Limitations

This study has several limitations. Firstly, while reliability of the protocol was shown to be excellent for both Chinese and Caucasian groups, only OSA patients were included in this study. In future, the reliability of the protocol for a larger population needs further investigation. Secondly, only one phantom based on CT images of a Chinese OSA patient rather than a Canadian OSA patient were used as a gold standard. However, to test the accuracy of the protocol, it is common to have one phantom as a gold standard.

The intra-observer and inter-observer reliability of 3D measurement of the nasal cavity using CBCT images were excellent. Therefore, the methodology of nasal cavity measurement used in this study is recommended in future studies on CBCT images. The 3D anthropomorphic phantom that was used in this study offered a feasible method to validate the measurement of the nasal cavity on CBCT images.

A. Author Contribution

Hui Chen, study design, wrote the manuscript, prepared figures and tables;

Tao Lv, study design, revise the manuscript;

Qing Luo, analyzed the images, statistical analysis;

Lei Li, 3D printing; prepared the phantom;

Qing Wang, analyzed the data;

Yanzhong Li, 3D analysis of nasal cavity; prepared figures and tables;

Debo Zhou, analyzed the data, prepared the phantom;

Elham Emami, wrote and revised the manuscript;

Matthieu Schmittbuhl, wrote and revise the manuscript;

Paul van der Stelt, wrote the manuscript;

Nelly Huynh, study design, wrote and revise the manuscript.

B. Ethics Approval and Consent to Participate

This is a retrospective study based on the CBCT images of both Canadian and Chinese OSA patients.

The protocol of recruiting Canadian OSA patients was approved by the Research Ethics Board of Health of the Université de Montréal (13-076-CERES-D).

The protocol of recruiting patients from China was approved by the Medical Ethic Committee of the Dental School of Shandong University (NO.GR201814).

C. Funding

This work was supported by Canadian Institutes of Health Research (grant 325899), the Canadian Foundation of Innovation (CFI 28236), Chinese Stomatological Association Clinical Research Fund (CSA-O2022-08), Shandong Provincial Natural Science Foundation (grant N.ZR2020QH161), China Oral Health Foundation (grant A2021-102), China Postdoctoral Science Foundation, grant number(2020M682173).

D. Conflict of Interests

All authors declare that they have no conflict of interests.

Patel RG (2017) Nasal Anatomy and Function. Facial Plast Surg 33(1):3-8. https://doi: 10.1055/s-0036-1597950.
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. Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual Conference. 2007:5505-8. http://doi: 10.1109/iembs.2007.4353592.
Lofaso F, Coste A, d'Ortho MP, Zerah-Lancner F, Delclaux C, Goldenberg F, Harf A (2000) Nasal obstruction as a risk factor for sleep apnoea syndrome. Eur Respir J.16(4):639-43. PubMed PMID: 11106205.
McNicholas WT (2008) The nose and OSA: variable nasal obstruction may be more important in pathophysiology than fixed obstruction. European Respir J 32(1):3-8. http://doi: 10.1183/09031936.00050208.
Kohler M, Bloch KE, Stradling JR (2007) The role of the nose in the pathogenesis of obstructive sleep apnoea and snoring. Eur Respir J 30(6):1208-15. http://doi: 10.1183/09031936.00032007.
Sériès F, St Pierre S, Carrier G (1993) Surgical correction of nasal obstruction in the treatment of mild sleep apnoea: importance of cephalometry in predicting outcome. Thorax 48(4):360-3. http://doi: 10.1136/thx.48.4.360.
Pang KP (2013) The Role of Nasal Surgery in the Treatment of OSA. Curr Otorhinolaryngol Rep 1(1):20-4. http://doi: 10.1007/s40136-012-0007-0.
Hoel HC, Kvinnesland K, Berg S (2020) Impact of nasal resistance on the distribution of apneas and hypopneas in obstructive sleep apnea. Sleep Med 71:83-8. http://doi: 10.1016/j.sleep.2020.03.024.
Keeler JA, Patki A, Woodard CR, Frank-Ito DO (2016) A Computational Study of Nasal Spray Deposition Pattern in Four Ethnic Groups. J Aerosol Med Pulm Drug Deliv 29(2):153-66. http://doi: 10.1089/jamp.2014.1205.
Tingelhoff K, Moral AI, Kunkel ME, Rilk M, Wagner I, Eichhorn KWG, Wahl FM, Bootz F (2007) Comparison between Manual and Semi-automatic Segmentation of Nasal Cavity and Paranasal Sinuses from CT Images. Annu Int Conf IEEE Eng Med Biol Soc 2007:5505-8. http://doi: 10.1109/IEMBS.2007.4353592.
Guijarro-Martínez R, Swennen GR (2011) Cone-beam computerized tomography imaging and analysis of the upper airway: a systematic review of the literature. Int J Oral Maxillofac Surg 40(11):1227-37. http://doi: 10.1016/j.ijom.2011.06.017.
Dawood A, Patel S, Brown J (2009) Cone beam CT in dental practice. Br Dent J 207:23. http://doi: 10.1038/sj.bdj.2009.560.
Alsufyani NA, Hess A, Noga M, Ray N, Al-Saleh MAQ, Lagravère 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 Dentofacial Orthop 150(4):703-12. https://doi.org/10.1016/j.ajodo.2016.06.024.
Chen H, van Eijnatten M, Wolff J, de Lange J, van der Stelt PF, Lobbezoo F, Aarab G (2017) Reliability and accuracy of three imaging software packages used for 3D analysis of the upper airway on cone beam computed tomography images. Dentomaxillofac Radiol 46(6):20170043. http://doi: 10.1259/dmfr.20170043.
Emami E, Lavigne G, Feine JS, Karp I, Rompré PH, Almeida FR, Huynh NT (2021) Effects of nocturnal wearing of dentures on the quality of sleep and oral-health-related quality in edentate elders with untreated sleep apnea: a randomized cross-over trial. Sleep 44(10). http://doi: 10.1093/sleep/zsab101.
Walter SD EM, Donner A (1998) Sample size and optimal designs for reliability studies. Stat Med 17:101-110. http:// doi: 10.1002/(sici)1097-0258(19980115)17:1<101::aid-sim727>3.0.co;2-e.
Chen H, Aarab G, Parsa A, de Lange J, van der Stelt PF, Lobbezoo F (2016) Reliability of three-dimensional measurements of the upper airway on cone beam computed tomography images. Oral Surg Oral Med Oral Pathol Oral Radiol 122(1):104-10. http://doi: 10.1016/j.oooo.2016.04.005.
Venkatesh E, Elluru SV (2017) Cone beam computed tomography: basics and applications in dentistry. J Istanb Univ Fac Dent 51(3 Suppl 1):S102-s21. http://doi: 10.17096/jiufd.00289.
von Arx T, Lozanoff S, Bornstein MM (2019) Extraoral anatomy in CBCT - a literature review. Part 1: Nasoethmoidal region. Swiss Dent J 129(10):804-15.
Cai Y, Goldberg AN, Chang JL (2020) The Nose and Nasal Breathing in Sleep Apnea. Otolaryngol Clin North Am 53(3):385-95. http://doi: 10.1016/j.otc.2020.02.002.
Mekhitarian Neto L, Fava AS, Lopes HC, Stamm A(2005) Epidemiological analysis of structural alterations of the nasal cavity associated with obstructive sleep apnea syndrome (OSA). Braz J Otorhinolaryngol 71(4):464-6. http://doi: 10.1016/s1808-8694(15)31200-3.
Bui NL, Ong SH, Foong KWC (2015) Automatic segmentation of the nasal cavity and paranasal sinuses from cone-beam CT images. International Journal of Computer Assisted Radiology and Surgery 10(8):1269-77. http://doi: 10.1007/s11548-014-1134-5.
Keustermans W, Huysmans T, Schmelzer B, Sijbers J, Dirckx JJ (2019) Matlab(®) toolbox for semi-automatic segmentation of the human nasal cavity based on active shape modeling. Comput Biol Med 105:27-38. http://doi: 10.1016/j.compbiomed.2018.12.008.
Hafezeqoran A, Rahbar M, Koodaryan R, Molaei T (2021) Comparing the Dimensional Accuracy of Casts Obtained from Two Types of Silicone Impression Materials in Different Impression Techniques and Frequent Times of Cast Preparation. Int J Dent 2021:9977478. http://doi: 10.1155/2021/9977478.
Pham DL, Xu C, Prince JL (2000) Current methods in medical image segmentation. Annu Rev Biomed Eng 2:315-37. http://doi: 10.1146/annurev.bioeng.2.1.315.

No competing interests reported.

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Reliability and accuracy of a semi-automatic segmentation protocol of the nasal cavity using cone beam computed tomography in patients with sleep apnea

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