Fine structural human phantom in dentistry and instance tooth segmentation.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
02 Jun 2024
02 Jun 2024
Historique:
received:
01
01
2024
accepted:
28
05
2024
medline:
2
6
2024
pubmed:
2
6
2024
entrez:
1
6
2024
Statut:
epublish
Résumé
In this study, we present the development of a fine structural human phantom designed specifically for applications in dentistry. This research focused on assessing the viability of applying medical computer vision techniques to the task of segmenting individual teeth within a phantom. Using a virtual cone-beam computed tomography (CBCT) system, we generated over 170,000 training datasets. These datasets were produced by varying the elemental densities and tooth sizes within the human phantom, as well as varying the X-ray spectrum, noise intensity, and projection cutoff intensity in the virtual CBCT system. The deep-learning (DL) based tooth segmentation model was trained using the generated datasets. The results demonstrate an agreement with manual contouring when applied to clinical CBCT data. Specifically, the Dice similarity coefficient exceeded 0.87, indicating the robust performance of the developed segmentation model even when virtual imaging was used. The present results show the practical utility of virtual imaging techniques in dentistry and highlight the potential of medical computer vision for enhancing precision and efficiency in dental imaging processes.
Identifiants
pubmed: 38824210
doi: 10.1038/s41598-024-63319-x
pii: 10.1038/s41598-024-63319-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
12630Subventions
Organisme : Japan Society for the Promotion of Science
ID : 23K07084
Organisme : JST A-STEP
ID : JPMJTM22E4
Informations de copyright
© 2024. The Author(s).
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