A new segmentation algorithm for measuring CBCT images of nasal airway: a pilot study

Abstract

BackgroundThree-dimensional (3D) modeling of the nasal airway space is becoming increasingly important for assessment in breathing disorders. Processing cone beam computed tomography (CBCT) scans of this region is complicated, however, by the intricate anatomy of the sinuses compared to the simpler nasopharynx. A gold standard for these measures also is lacking. Previous work has shown that software programs can vary in accuracy and reproducibility outcomes of these measurements. This study reports the reproducibility and accuracy of an algorithm, airway segmentor (AS), designed for nasal airway space analysis using a 3D printed anthropomorphic nasal airway model.MethodsTo test reproducibility, two examiners independently used AS to edit and segment 10 nasal airway CBCT scans. The intra- and inter-examiner reproducibility of the nasal airway volume was evaluated using paired t-tests and intraclass correlation coefficients. For accuracy testing, the CBCT data for pairs of nasal cavities were 3D printed to form hollow shell models. The water-equivalent method was used to calculate the inner volume as the gold standard, and the models were then embedded into a dry human skull as a phantom and subjected to CBCT. AS, along with the software programs MIMICS 19.0 and INVIVO 5, was applied to calculate the inner volume of the models from the CBCT scan of the phantom. The accuracy was reported as a percentage of the gold standard.ResultsThe intra-examiner reproducibility was high, and the inter-examiner reproducibility was clinically acceptable. AS and MIMICS presented accurate volume calculations, while INVIVO 5 significantly overestimated the mockup of the nasal airway volume.ConclusionWith the aid of a 3D printing technique, the new algorithm AS was found to be a clinically reliable and accurate tool for the segmentation and reconstruction of the nasal airway space.

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“…But a substantially lower accuracy was found in volume calculation, with a little underestimated volume compared to direct measurement (Table ). This was consistent with the previous reports by Zhang et al, who demonstrated greatly overestimated volume of nasal cavity with measurement errors + 3.9% in the left side and 7.6% in the right side when segmenting nasal airway . An explanation for this could be the limitation of semi‐automated segmentation method.…”

“…No correlation was found between the mean and the difference of the four variables from the plots, and the results were uniformly distributed within the limits of agreement, which demonstrated that there existed a consistent agreement between the two methods. 19 An explanation for this could be the limitation of semi-automated segmentation method. Small edges could be removed by morphology operation or multiple slice edit manually.…”

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“…In addition, the segmentation could be completed within 1 hour, which is considerably less compared to a fully manual segmentation, 5 although comparable with what was reported in the literature for semiautomatic segmentation. 14,15 Volume measurements for the NC demonstrated good correlation coefficients and low measurement errors, indicating excellent intraexaminer and interexaminer reliabilities.…”

“…1,12 OSA is suggested to be related to a PA anatomic disorder 13 so that the treatment options are mostly associated with enlarging the PA. 12 CBCT scans taken for orthodontic purposes represent a low-dose 3D radiological record facilitating the To date, several authors have reported methods to measure the NC. 6,14,15 However, automatic segmentation methods using commercial dental software were reported to be inaccurate. 16 In this study, the masks were sketched manually every 10 slides and then a semiautomatic procedure was followed.…”

AI-based automatic segmentation of craniomaxillofacial anatomy from CBCT scans for automatic detection of pharyngeal airway evaluations in OSA patients

“…Following an extensive literature research, we have given the ICC values of 5 studies that compared the segmentation of AI and ground truth in Table 4 . ICC values were reported as 0.899 by Zhang et al 45 , 0.977 by Leonardi et al 46 , 0.985 by Sin et al 12 , 0.986 by Park et al 47 . Shujaat et al 48 provided precision, recall, accuracy, dice, intersection over union values in their study as 0.97 ± 0.01, 0.96 ± 0.03 1.00 ± 0.00 0.97 ± 0.02 and 0.93 ± 0.03, respectively.…”