An effective teeth recognition method using label tree with cascade network structure

In this article, we apply the deep learning technique to medical field for the teeth detection and classification of dental periapical radiographs, which is important for the medical curing and postmortem identification. We detect teeth in an input X-ray image and distinguish them from different position. An adult usually has 32 teeth, and some of them are similar while others have very different shape. So there are 32 teeth position for us to recognize, which is a challenging task. Convolutional neural network is a popular method to do multi-class detection and classification, but it needs a lot of training data to get a good result if used directly. The lack of data is a common case in medical field due to patients' privacy. In this work, limited to the available data, we propose a new method using label tree to give each tooth several labels and decompose the task, which can deal with the lack of data. Then use cascade network structure to do automatic identification on 32 teeth position, which uses several convolutional neural network as its basic module. Meanwhile, several key strategies are utilized to improve the detection and classification performance. Our method can deal with many complex cases such as X-ray images with tooth loss, decayed tooth and filled tooth, which frequently appear on patients. The experiments on our dataset show: for small training dataset, compared to the precision and recall by training a 33-classes (32 teeth and background) state-of-the-art convolutional neural network directly, the proposed approach reaches a high precision and recall of 95.8% and 96.1% in total, which is a big improvement in such a complex task.