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Zhang C, Bruggink R, Baan F, et al. A new segmentation algorithm for measuring CBCT images of nasal airway: a pilot study. PeerJ. 2019;7:e6246doi: 10.7717/peerj.6246.
Zhang, C., Bruggink, R., Baan, F., Bronkhorst, E., Maal, T., He, H., & Ongkosuwito, E. M. (2019). A new segmentation algorithm for measuring CBCT images of nasal airway: a pilot study. PeerJ, 7e6246. https://doi.org/10.7717/peerj.6246
Zhang, Chen, et al. "A new segmentation algorithm for measuring CBCT images of nasal airway: a pilot study." PeerJ vol. 7 (2019): e6246. doi: https://doi.org/10.7717/peerj.6246
Zhang C, Bruggink R, Baan F, Bronkhorst E, Maal T, He H, Ongkosuwito EM. A new segmentation algorithm for measuring CBCT images of nasal airway: a pilot study. PeerJ. 2019 Jan 28;7:e6246. doi: 10.7717/peerj.6246. eCollection 2019. PMID: 30713816; PMCID: PMC6354662.
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PeerJ. 2019 Jan 28;7:e6246. doi: 10.7717/peerj.6246. eCollection 2019.

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

PeerJ

Chen Zhang, Robin Bruggink, Frank Baan, Ewald Bronkhorst, Thomas Maal, Hong He, Edwin M Ongkosuwito

Affiliations

  1. The 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, China.
  2. Department of Dentistry, Section of Orthodontics and Craniofacial Biology, Radboud University Nijmegen Medical Center, Radboud University Nijmegen, Nijmegen, Netherlands.
  3. 3DLAB The Netherlands, Radboud University Medical Center, Radboud University Nijmegen, Nijmegen, Netherlands.
  4. Department of Dentistry, Section of Preventive and Restorative Dentistry, Radboud University Nijmegen Medical Center, Radboud University Nijmegen, Nijmegen, Netherlands.
  5. Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Center, Radboud University Nijmegen, Nijmegen, Netherlands.

PMID: 30713816 PMCID: PMC6354662 DOI: 10.7717/peerj.6246

Abstract

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

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.

Keywords: 3D printing; Airway model; CBCT; Image segmentation; Nasal airway

Conflict of interest statement

The authors declare that they have no competing interests.

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