DentalSegmentator: Robust open source deep learning-based CT and CBCT image segmentation.

OBJECTIVES: Segmentation of anatomical structures on dento-maxillo-facial (DMF) computed tomography (CT) or cone beam computed tomography (CBCT) scans is increasingly needed in digital dentistry. The main aim of this research was to propose and evaluate a novel open source tool called DentalSegmentator for fully automatic segmentation of five anatomical structures on DMF CT and CBCT scans: maxilla/upper skull, mandible, upper teeth, lower teeth, and the mandibular canal. METHODS: A retrospective sample of 470 CT and CBCT scans was used as a training/validation set. The performance and generalizability of the tool was evaluated by comparing segmentations provided by experts and automatic segmentations in two hold-out test datasets: an internal dataset of 133 CT and CBCT scans acquired before orthognathic surgery and an external dataset of 123 CBCT scans randomly sampled from routine examinations in 5 institutions. RESULTS: The mean overall results in the internal test dataset (n = 133) were a Dice similarity coefficient (DSC) of 92.2 ± 6.3 % and a normalised surface distance (NSD) of 98.2 ± 2.2 %. The mean overall results on the external test dataset (n = 123) were a DSC of 94.2 ± 7.4 % and a NSD of 98.4 ± 3.6 %. CONCLUSIONS: The results obtained from this highly diverse dataset demonstrate that this tool can provide fully automatic and robust multiclass segmentation for DMF CT and CBCT scans. To encourage the clinical deployment of DentalSegmentator, the pre-trained nnU-Net model has been made publicly available along with an extension for the 3D Slicer software. CLINICAL SIGNIFICANCE: DentalSegmentator open source 3D Slicer extension provides a free, robust, and easy-to-use approach to obtaining patient-specific three-dimensional models from CT and CBCT scans. These models serve various purposes in a digital dentistry workflow, such as visualization, treatment planning, intervention, and follow-up.

Comparative effect of anthocyanin on proliferation and migration of human gingival fibroblasts in the absence or presence of nicotine.

Background: Oral fibroblast malfunction can result in periodontal diseases. Nicotine can prolong the healing process as an irritant of oral tissues. Anthocyanins have been demonstrated to have potential benefits in preventing or treating smoking-related periodontal diseases. Cyanidin chloride's (CC's) potential in oral wound healing and the viability, proliferation, and migration of human gingival fibroblasts (HGFs) were examined in the presence and absence of nicotine by an in vitro study. Methods: The effects of different nicotine concentrations (1, 2, 3, 4, and 5 mM) on the viability and proliferation of HGF cells were evaluated in the presence and absence of different CC concentrations (5, 10, 25, and 50 µM) using the quantitative MTT assay. The scratch test was performed to evaluate the migration of CC-treated cells in the presence of 2.5-mM nicotine. Results: No cytotoxicity was observed at 1‒100 µM CC concentrations after 24, 48, and 72 hours of exposure to HGF cells. However, a concentration of 200 µM significantly reduced cell viability by about 20% at all the three-time intervals (P<0.05). Also, 3‒5-mM concentrations of nicotine significantly reduced cell viability in a dose- and time-dependent manner. Moreover, the understudied CC concentrations decreased nicotine's adverse effects on cell migration to some extent. Conclusion: Although the understudied CC concentrations could not significantly reduce the adverse effects of understudied nicotine concentrations on the viability and proliferation of HGF cells, they were able to reduce the detrimental effects of nicotine on cell migration significantly.