Abstract:
Teeth movement is an important process for a dentist that helps in gauging
the progress of the treatment. However, the lack of a stable reference with
respect to which one could measure the teeth movement makes this a challenging
problem. In this work, the Rugea are used as stable reference on which a
segmentation and motion measurement of all individual teeth in the upper jaw
is performed. The approach in this work is to utilize deep learning Convolutional
Neural Networks (CNNs) to segment the rugae and the individual teeth.
Building upon the robustness of two-dimensional image semantic segmentation,
this work develops a method to convert three-dimensional textured scans of the
upper palate to two-dimensional data on which the semantic segmentation is applied.
Moreover, the achieved two-dimensional segmentation is pulled-back to
segment the original three-dimensional textured mesh. After the segmentation of
two three-dimensional scans of the same patient before and after an orthodontic
treatment, an algorithm is developed to match the scans at the stable rugae region
from which the three-dimensional, translation and rotation, motion of the
individual teeth is computed.
Advisor(s):
Shammas, Elie; Asmar, Daniel; Sakr, Georges
