Journal of dental sciences
Active learning-assisted deep learning for automated maxillary sinus segmentation in dental cone-beam computed tomography
By Chen H, Nguyen KD, Chang CL and 7 others.
Abstract
Background/purpose
Accurate automated segmentation of the maxillary sinus in cone-beam computed tomography (CBCT) scans is crucial for dental implant planning, sinus lift procedures, and pathological assessment, yet there remains a notable lack of publicly available models and datasets for this task. This study developed and evaluated a deep learning model for automated maxillary sinus segmentation using an active learning approach to minimize annotation effort while achieving high accuracy.
Materials and methods
We utilized 249 CBCT scans from the publicly available CTooth+ and STS-Tooth datasets. Ground truth annotations were generated through semi-automated segmentation in 3D Slicer with validation by five expert dentists. An active learning strategy was employed using the nnU-Net 3D full-resolution architecture trained with five-fold cross-validation. Performance was evaluated using Dice similarity coefficient (Dice).