Accurate and automatic tooth image segmentation model with deep convolutional neural networks and level set method

In oral surgery, accurate segmentation of the teeth has critical significance for the orthodontic treatment and research. However, in the cone beam computed tomography (CBCT) images, the boundaries of the teeth are blurred and some tissues around the teeth have similar intensities to the teeth, which makes the tooth segmentation more difficult and challenging. In this paper, an accurate and automatic active contour model is proposed for the tooth segmentation. First, we apply deep convolutional neural networks to automatically detect the approximate position of each dental pulp. Then, we take the barycenter point of the pixels in the marked area as the center of the tooth. Based on this, we design the shape prior information by a series of mathematical methods to describe the shape, size and position of the tooth, which is achieved by further detecting the direction and length of the tooth. To make full use of the shape prior information, we define the prior constraint term to limit the segmentation curve to evolve around the shape prior information, while making the segmentation contour as close as possible to the shape prior information. Last, combining the image data term, the length term, the regularization term and the prior constraint term, we give the level set formulation of the energy functional and minimize it by the steepest descent method. To test the feasibility and effectiveness of the proposed model, we apply the proposed model to segment the tooth images in different slices. Experimental results show that the proposed model can accurately segment the tooth images. Qualitative comparison results demonstrate the proposed model is superior to the CV model, the RSF model, the LGIF model, the LIC model and the U-Net model in terms of the segmentation accuracy. In addition, the sensitivity test verifies that the proposed model is insensitive to the initial contours and deep network outputs.