  Dental panoramic X-ray imaging is a popular diagnostic method owing to its
very small dose of radiation. For an automated computer-aided diagnosis system
in dental clinics, automatic detection and identification of individual teeth
from panoramic X-ray images are critical prerequisites. In this study, we
propose a point-wise tooth localization neural network by introducing a spatial
distance regularization loss. The proposed network initially performs center
point regression for all the anatomical teeth (i.e., 32 points), which
automatically identifies each tooth. A novel distance regularization penalty is
employed on the 32 points by considering $L_2$ regularization loss of Laplacian
on spatial distances. Subsequently, teeth boxes are individually localized
using a cascaded neural network on a patch basis. A multitask offset training
is employed on the final output to improve the localization accuracy. Our
method successfully localizes not only the existing teeth but also missing
teeth; consequently, highly accurate detection and identification are achieved.
The experimental results demonstrate that the proposed algorithm outperforms
state-of-the-art approaches by increasing the average precision of teeth
detection by 15.71% compared to the best performing method. The accuracy of
identification achieved a precision of 0.997 and recall value of 0.972.
Moreover, the proposed network does not require any additional identification
algorithm owing to the preceding regression of the fixed 32 points regardless
of the existence of the teeth.
