In dental implantology, precise delineation of maxillary sinuses and inferior alveolar nerves (IAN) on CBCT scans is essential for implant planning. Addressing the time-consuming manual segmentation, we introduce SISTR (Sinus and IAN Segmentation with Targeted Refinement), a novel deep-learning method for automated, precise segmentation. SISTR employs a two-stage approach: initially, it predicts coarse segmentation and offset maps to anatomical regions, followed by clustering for region centroids identification and targeted cropping for refined segmentation. Developed on the most diverse dataset to date for sinus and IAN segmentation, sourced from 11 dental clinics and 10 manufacturers (358 CBCT volumes for sinus, 499 for IAN), SISTR demonstrates robust generalizability. It achieved strong performance on an external test set, reaching average DICE scores of 96.64% (95.38-97.60) for sinus and 83.43% (80.96-85.63) for IAN, marking a significant 10 percentage point improvement in Dice Score for IAN compared to single-stage methods (p-value < 0.005). Chamfer distances of 0.38 (0.24-0.60) mm for sinus and 0.88 (0.58-1.27) mm for IAN affirm its precision. Efficient in fast and precise segmentation with an inference time of 4 seconds per case, SISTR advances implant planning in digital dentistry.

The authors have declared no competing interest.

This study did not receive any funding.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Data were collected from centers representing French dental practices. In compliance with GDPR, all data were anonymized in situ at each practice, negating the need for individual patient consent and ensuring that the information ceased to be personal. Our study exclusively uses this deidentified dataset.

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The full datasets are protected because of privacy issues and regulation policies in dental clinics. However part of the data is available in the following public domain resources: https://pan.baidu.com/s/1LdyUA2QZvmU6ncXKl_bDTw, and https://ditto.ing.unimore.it/maxillo/dataset/