Dataset Information

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

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 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.

SUBMITTER: Zhang C

PROVIDER: S-EPMC6354662 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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

Zhang Chen C Bruggink Robin R Baan Frank F Bronkhorst Ewald E Maal Thomas T He Hong H Ongkosuwito Edwin M EM

PeerJ 20190128

<h4>Background</h4>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 s ...[more]

PMID: 30713816

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Project description:Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate three-dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal-to-noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems.To segment CBCT images, the authors propose a new method for fully automated CBCT segmentation by using patch-based sparse representation to (1) segment bony structures from the soft tissues and (2) further separate the mandible from the maxilla. Specifically, a region-specific registration strategy is first proposed to warp all the atlases to the current testing subject and then a sparse-based label propagation strategy is employed to estimate a patient-specific atlas from all aligned atlases. Finally, the patient-specific atlas is integrated into a maximum a posteriori probability-based convex segmentation framework for accurate segmentation.The proposed method has been evaluated on a dataset with 15 CBCT images. The effectiveness of the proposed region-specific registration strategy and patient-specific atlas has been validated by comparing with the traditional registration strategy and population-based atlas. The experimental results show that the proposed method achieves the best segmentation accuracy by comparison with other state-of-the-art segmentation methods.The authors have proposed a new CBCT segmentation method by using patch-based sparse representation and convex optimization, which can achieve considerably accurate segmentation results in CBCT segmentation based on 15 patients.

Dataset Information

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

Publications

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

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