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Accurate segmentation of the teeth from Cone Beam Computed Tomography (CBCT) images is a critical step towards building the personalized 3D digital model as it can provide important information to orthodontists for clinical treatments. However, the teeth CBCT image segmentation is a challenging task, especially for the root parts, because the root contour of a tooth may be degenerated by noise and surrounding alveolar bone or neighboring teeth. Most existing methods employ semi-automatic or interactive methods and there are few automatic and high-precision methods for teeth root segmentation. In this paper, we design a lightweight CNN architecture to accomplish this task as an end-to-end framework which can automatically segment the teeth from CBCT images. Specifically, we use ordinary convolutions, dilated convolutions and residual connections as the basic module to build the network. After that, a geodesic active contour model is employed to refine the CNN’s outputs which can further improve the segmentation results. The whole pipeline is fully automatic and without any image-specific fine tune. The method is evaluated on a dental CBCT segmentation challenge and achieves state-of-the-art results.
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