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Tooth automatic segmentation from CBCT images: a systematic review

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Clinical Oral Investigations Aims and scope Submit manuscript

Abstract

Objectives

To describe the current state of the art regarding technological advances in full-automatic tooth segmentation approaches from 3D cone-beam computed tomography (CBCT) images.

Materials and methods

In March 2023, a search strategy without a timeline setting was carried out through a combination of MeSH terms and free text words pooled through Boolean operators (‘AND’, ‘OR’) on the following databases: PubMed, Scopus, Web of Science and IEEE Explore. Randomized and non-randomized controlled trials, cohort, case–control, cross-sectional and retrospective studies in the English language only were included.

Results

The search strategy identified 541 articles, of which 23 have been selected. The most employed segmentation methods were based on deep learning approaches. One article exposed an automatic approach for tooth segmentation based on a watershed algorithm and another article used an improved level set method. Four studies presented classical machine learning and thresholding approaches. The most employed metric for evaluating segmentation performance was the Dice similarity index which ranged from 90 ± 3% to 97.9 ± 1.5%.

Conclusions

Thresholding appeared not reliable for tooth segmentation from CBCT images, whereas convolutional neural networks (CNNs) have been demonstrated as the most promising approach. CNNs could help overcome tooth segmentation’s main limitations from CBCT images related to root anatomy, heavy scattering, immature teeth, metal artifacts and time consumption. New studies with uniform protocols and evaluation metrics with random sampling and blinding for data analysis are encouraged to objectively compare the different deep learning architectures’ reliability.

Clinical relevance

Automatic tooth segmentation’s best performance has been obtained through CNNs for the different ambits of digital dentistry.

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Data availability

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Author information

Authors and Affiliations

  1. Department of General Surgery and Surgical-Medical Specialties, School of Dentistry, University of Catania, AOU “Policlinico-San Marco”, Via S. Sofia 78, 95124, Catania, Italy

    Alessandro Polizzi, Vincenzo Ronsivalle, Pietro Venezia, Simona Santonocito, Antonino Lo Giudice, Rosalia Leonardi & Gaetano Isola

  2. Department of Life, Health & Environmental Sciences, Postgraduate School of Orthodontics, University of L’Aquila, 67100, L’Aquila, Italy

    Alessandro Polizzi & Vincenzo Quinzi

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  1. Alessandro Polizzi
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Contributions

A.P., P.V. and G.I. selected and read the papers included in this systematic review and drafted the manuscript. A.L.G., G.I., R.L. and V.Q. read the papers included in this systematic review, analysed and interpreted the data. V.R. and S.S. analysed the data and gave scientific support. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Alessandro Polizzi.

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Polizzi, A., Quinzi, V., Ronsivalle, V. et al. Tooth automatic segmentation from CBCT images: a systematic review. Clin Oral Invest 27, 3363–3378 (2023). https://doi.org/10.1007/s00784-023-05048-5

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