Toothpix: Pixel-Level Tooth Segmentation in Panoramic X-Ray Images based on Generative Adversarial Networks

Abstract

Accurate tooth segmentation in panoramic X-ray images is an essential stage before clinical surgery. This paper presents a deep segmentation network ToothPix, which leverages Generative Adversarial Network structures to exploit comprehensive semantic information for tooth segmentation. We introduce wide residual blocks and an encoder-decoder structure into the generator of ToothPix, which can learn grayscale and boundary features of teeth guided by a fully convolutional network discriminator. Without fine-grained ground truths, the losses in ToothPix guide the extraction of features to confuse the discriminator while effectively avoiding network overfitting. Furthermore, ToothPix generates small patches from whole panoramic X-ray images by a combination of image transformations, to increase the diversity of samples and reduce the computation. Experimental results demonstrate that our method outperforms state-of-the-art methods on the LNDb dental dataset.