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Войти в исследования Dento maxillo facial radiology · 2026
SinusNet+: Deep Condition-Label-Free Segmentation of Maxillary Sinus Conditions in CBCT images.
СтатьяНазвание и аннотация: Перевод в очереди
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Kim Da-El, Yang Su, Lim Sang Heon, Han Ji Yong, Kim Sujeong, Kim Jun-Min, Lee Sang-Jeong, Kim Jo-Eun, Huh Kyung-Hoe, Lee Sam-Sun, Heo Min-Suk, Yi Won-Jin
Аннотация
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.
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.