SinusNet+: Deep Condition-Label-Free Segmentation of Maxillary Sinus Conditions in CBCT images

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ID: 316947
2026
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Ranked #26 of 46 articles by views in dentomaxillofacial radiology

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Abstract
Abstract Background Segmentation of maxillary sinus conditions (MSC) in cone-beam computed tomography (CBCT) images may support preoperative assessment in the posterior maxilla, including implant planning and sinus floor augmentation. Supervised deep learning methods for MSC segmentation typically rely on labor-intensive manual annotation of MSC for network training. Objectives This study aimed to develop and evaluate a condition-label-free deep learning framework (SinusNet+) for MSC segmentation in CBCT images, in which network training does not require manual MSC annotations and instead relies on synthetic conditions generated within the normal maxillary sinus (MS). Methods To generate synthetic MSC in normal MS, a synthetic condition generator was introduced to simulate MSC within the normal MS by varying texture, shape, and noise, thereby approximating a range of radiographic appearances of MSC in CBCT images. Results SinusNet+ achieved an average Dice similarity coefficient of 0.820±0.110, precision of 0.878±0.061, and recall of 0.777±0.145, respectively. The proposed method outperformed unsupervised baselines and achieved segmentation performance comparable to that of the supervised approach. Conclusions The proposed framework demonstrates the feasibility of condition-label-free segmentation of MSC in CBCT images, while still requiring anatomical annotation of the normal MS during dataset preparation.
Reference Key
openalex_W7164299365 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors D M Kim, Su Yang, Sang Heon Lim, Ji Yong Han, Sujeong Kim, Jun-Min Kim, Sang-Jeong Lee, Jo-Eun Kim, Kyung-Hoe Huh, S S Lee, Min-Suk Heo, Won-Jin Yi
Journal dentomaxillofacial radiology
Year 2026
DOI
10.1093/dmfr/twag040
URL
https://doi.org/10.1093/dmfr/twag040
Keywords Keywords not found

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