Download In this abstract a statistical model-based method is presented for the

AUTOMATED SEGMENTATION OF TEETH FROM
CBCT IMAGES
Johannes Keustermans1, Wouter Mollemans1, Filip Schutyser1, Dirk Vandermeulen1 and Paul
Suetens1
1
K.U.Leuven, Faculty of Engineering, ESAT/PSI, Medical Image Computing
UZ Gasthuisberg, Herestraat 49 bus 7003, 3000 Leuven, Belgium
ABSTRACT
AIM
The introduction of Cone-Beam Computed Tomography (CBCT) has instigated a breakthrough for
the routine computer-aided planning of orthognatic surgery or orthodontic treatment, due to its
low radiation dose, unique accessibility and low cost. These applications require the segmentation
of certain anatomical structures of interest, like bone and soft tissue. The segmentation of teeth
from the images is of particular interest for certain applications, e.g. virtual tooth extraction and
orthodontic treatment planning and evaluation. Yet, the segmentation of teeth is hampered by
mainly two factors. Firstly, the presence of metal streak artifacts caused by orthodontic braces or
dental fillings, and secondly, since the teeth are anchored in the jaw bone there is only little
contrast between the bone and the teeth, predominantly at the level of the apex. In this abstract, we
present an automated method for the segmentation of teeth from CBCT images.
MATERIALS AND METHODS
The presented method incorporates prior information on the individual teeth, captured from a set
of training data consisting of CBCT images with manually segmented teeth. This prior information
is twofold. Firstly, the overall shape of each tooth is captured by a statistical model, describing the
average shape and its possible variations. Secondly, the image information is captured by
approximating the grey values present in the image surrounding each tooth by a constant grey
value inside and a constant grey value outside of each tooth. Therefore the presented method
consists of two phases. In the first phase, the statistical model describing the shape of each
individual tooth is built, whereas in the second phase this model is used for the segmentation of the
respective tooth.
RESULTS
To judge the potential of this method, an initial validation study is performed. Therefore, this
method is applied to the segmentation of the upper left canine and the upper left first molar. A
training data set of 22 CBCT images (0.4x0.4x0.4mm) is used in which the upper left canine and the
upper left first molar are manually segmented. Based on a leave-one-out approach applied to the
training data, preliminary results are obtained. This approach consists of removing a single training
sample from the set of training data, building the statistical model describing the shape on the
remaining training data, and applying the method to the removed training sample. Using this
approach the potential and accuracy of the method is shown.
SUMMARY
In this abstract a statistical model-based method is presented for the automated segmentation of
teeth from CBCT images. As an initial test this method is applied to the segmentation of the upper
left canine and the upper left first molar. The results of this initial validation study show some
promising results, indicating the potential of this method. In the future we would like to try to
extend this method to the segmentation of other teeth, as well as performing a more extensive and
quantitative validation study.