  Accurate and automatic segmentation of three-dimensional (3D) individual
teeth from cone-beam computerized tomography (CBCT) images is a challenging
problem because of the difficulty in separating an individual tooth from
adjacent teeth and its surrounding alveolar bone. Thus, this paper proposes a
fully automated method of identifying and segmenting 3D individual teeth from
dental CBCT images. The proposed method addresses the aforementioned difficulty
by developing a deep learning-based hierarchical multi-step model. First, it
automatically generates upper and lower jaws panoramic images to overcome the
computational complexity caused by high-dimensional data and the curse of
dimensionality associated with limited training dataset. The obtained 2D
panoramic images are then used to identify 2D individual teeth and capture
loose- and tight- regions of interest (ROIs) of 3D individual teeth. Finally,
accurate 3D individual tooth segmentation is achieved using both loose and
tight ROIs. Experimental results showed that the proposed method achieved an
F1-score of 93.35% for tooth identification and a Dice similarity coefficient
of 94.79% for individual 3D tooth segmentation. The results demonstrate that
the proposed method provides an effective clinical and practical framework for
digital dentistry.
