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Segmentation of the Wisdom Tooth from CBCT Images
Researchers Christoph Jud
Project Partner Department of Oral Surgery, University of Basel
Funding SNF
Project Duration 1.10.2010 - 31.9.2013
This project is a collaborative approach of the Computer Science Department and the Department for Oral Surgery at the University Basel. Surgical extraction of wisdom teeth (third Molar) is the most commonly performed procedure in oral surgery. Depending on the anatomical position and the distance of the roots to the mandibular canal, this procedure implies the risk of injury of the inferior alveolar nerve. In the worst case this leads to a permanent loss of sensation in the lower lip and the chin.
With the introduction of Cone Beam Computed Tomography (CBCT), a new imaging technology is available, which allows to obtain a detailed representation of the patient's anatomy with minimal radiation dose. The resulting three dimensional images open new possibilities in the risk assessment and planning of these surgeries. The goal of this project is to develop a method for automatic segmentation of the wisdom tooth and the mandibular canal from the CBCT images. In parallel, the indicators for an injury of the inferior alveolar nerve given in these images are studied by experienced oral surgeons. The pre-operative risk assessment of the surgeons will be compared with the actual outcome of the surgery. Based on the experience of this study a software for automatic risk assessment from the images will be developed and evaluated on the collected data. For being able to perform an automatic risk analysis, we plan to perform segmentation of the mandibular canal and the wisdom tooth. Besides allowing for a risk assessment, having a segmentation of these tissues is of independent interest for the clinical application. It allows to generate a three-dimensional model of the detailed patient's anatomy which can support the surgery planning and serve as a visual aid for patient education.
Automatic segmentation of the wisdom tooth from CBCT is made difficult by the low contrast, which makes the distinction of different tissue types difficult, and the fact that individual teeth may be touching. We approach both problems by fitting a statistical shape model, which restricts the segmentation result to the normal anatomy of the tooth. A further complication which arises is that the the spatial orientation of the tooth can greatly vary. To address this issue we develop an algorithm for detecting feature points in the images, such as the tips of the roots and the center of the crown, which allow us to determining its spatial alignment. The same detection algorithm will be used for detection of the mandibular canal. The canal itself is not clearly visible in the images, but at some places the cortical rim around this canal can be detected. Given the information of the exact position at some points, we will use shortest path algorithms, with edge-weights depending on the image intensities, to determine the exact course of this canal.

Publications Using object probabilities in deformable model fitting
Christoph Jud and Thomas Vetter. IN: 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014 (Forthcoming)
  Variational Image Registration Using Inhomogeneous Regularization [pdf] 
Jud,Christoph; Lüthi,Marcel; Albrecht,Thomas; Schönborn, Sandro and Vetter, Thomas
Journal of Mathematical Imaging and Vision, Volume 48, 12 February 2014
  A unified approach to shape model fitting and non-rigid registration [paper]  [slides]
Marcel Lüthi, Christoph Jud and Thomas Vetter
IN: Prceedings of the 4th International Workshop on Machine Learning in Medical Imaging, LNCS 8184, pp.66-73 Nagoya, Japan, September 2013
DOI: http://dx.doi.org/10.1007/978-3-319-02267-3_9
  Using Landmarks as a Deformation Prior for Hybrid Image Registration [pdf]
Marcel Lüthi, Christoph Jud and Thomas Vetter
IN: Proceedings DAGM'11: 33nd Annual Symposium of the German Association for Pattern Recognition, Frankfurt, August 2011