A new segmentation algorithm for measuring CBCT images of nasal airway: a pilot study
Background Three-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...
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PeerJ Inc.
2019-01-01
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| author | Chen Zhang Robin Bruggink Frank Baan Ewald Bronkhorst Thomas Maal Hong He Edwin M. Ongkosuwito |
| author_facet | Chen Zhang Robin Bruggink Frank Baan Ewald Bronkhorst Thomas Maal Hong He Edwin M. Ongkosuwito |
| author_sort | Chen Zhang |
| collection | DOAJ |
| description | Background Three-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. Methods To 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. Results The 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. Conclusion With 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. |
| first_indexed | 2024-03-09T06:54:26Z |
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| last_indexed | 2024-03-09T06:54:26Z |
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| spelling | doaj.art-b431e2584ada465fbf15c9d2f1fbf6382023-12-03T10:07:36ZengPeerJ Inc.PeerJ2167-83592019-01-017e624610.7717/peerj.6246A new segmentation algorithm for measuring CBCT images of nasal airway: a pilot studyChen Zhang0Robin Bruggink1Frank Baan2Ewald Bronkhorst3Thomas Maal4Hong He5Edwin M. Ongkosuwito6The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, ChinaDepartment of Dentistry, Section of Orthodontics and Craniofacial Biology, Radboud University Nijmegen Medical Center, Radboud University Nijmegen, Nijmegen, NetherlandsDepartment of Dentistry, Section of Orthodontics and Craniofacial Biology, Radboud University Nijmegen Medical Center, Radboud University Nijmegen, Nijmegen, NetherlandsDepartment of Dentistry, Section of Preventive and Restorative Dentistry, Radboud University Nijmegen Medical Center, Radboud University Nijmegen, Nijmegen, Netherlands3DLAB The Netherlands, Radboud University Medical Center, Radboud University Nijmegen, Nijmegen, NetherlandsThe State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, ChinaDepartment of Dentistry, Section of Orthodontics and Craniofacial Biology, Radboud University Nijmegen Medical Center, Radboud University Nijmegen, Nijmegen, NetherlandsBackground Three-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. Methods To 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. Results The 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. Conclusion With 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.https://peerj.com/articles/6246.pdfImage segmentationNasal airwayCBCT3D printingAirway model |
| spellingShingle | Chen Zhang Robin Bruggink Frank Baan Ewald Bronkhorst Thomas Maal Hong He Edwin M. Ongkosuwito A new segmentation algorithm for measuring CBCT images of nasal airway: a pilot study PeerJ Image segmentation Nasal airway CBCT 3D printing Airway model |
| title | A new segmentation algorithm for measuring CBCT images of nasal airway: a pilot study |
| title_full | A new segmentation algorithm for measuring CBCT images of nasal airway: a pilot study |
| title_fullStr | A new segmentation algorithm for measuring CBCT images of nasal airway: a pilot study |
| title_full_unstemmed | A new segmentation algorithm for measuring CBCT images of nasal airway: a pilot study |
| title_short | A new segmentation algorithm for measuring CBCT images of nasal airway: a pilot study |
| title_sort | new segmentation algorithm for measuring cbct images of nasal airway a pilot study |
| topic | Image segmentation Nasal airway CBCT 3D printing Airway model |
| url | https://peerj.com/articles/6246.pdf |
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