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Published on: April 8, 2020
SinusNet+: deep condition-label-free segmentation of maxillary sinus conditions in cone-beam CT images
Da-El Kim1, Su Yang2,3, Sang Heon Lim1
1Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 08826, South Korea.
Objectives:
Segmentation of maxillary sinus conditions (MSC) in cone-beam CT (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).
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.
