Tooth automatic segmentation from CBCT images: a systematic review

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 segmenta- tion’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.