SinusNet+: Deep Condition-Label-Free Segmentation of Maxillary Sinus Conditions in CBCT images.
Authors
Affiliations (6)
Affiliations (6)
- Interdisciplinary Program in Bioengineering, Seoul National University, Gwanak-gu, Seoul, 08826, South Korea.
- Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, 41944, South Korea.
- AI-driven Medical Innovation Center, Kyungpook National University Hospital, Daegu, 41944, South Korea.
- Department of Electronics and Information Engineering, Hansung University, Seongbuk-gu, 02876, South Korea.
- Department of Artificial Intelligence, Tech University of Korea, Siheung, 15073, South Korea.
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Jongno-gu, 03080, South Korea.
Abstract
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. 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). 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. 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. 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.