Automatic Teeth Segmentation Using Attention U-Net

Abstract

Dental radiography plays a crucial role in clinical diagnosis, treatment, and prognosis. In recent years, researchers have explored cutting-edge technologies to develop automated systems that can analyze radiographic imagery and support medical practitioners. The field of Artificial Intelligence (AI) has witnessed rapid advancements, with various approaches being developed or improved upon. While Convolutional Neural Networks (CNNs) have been widely used in medical image segmentation, the U-Net architecture has emerged as a standout performer due to its exceptional segmentation capabilities. This paper presents a proof of concept for the Attention U-Net archi-tecture applied to the task of teeth segmentation. The study demonstrates the superior performance of this network in accurately segmenting teeth using a newly available benchmark dataset called Tufts Dental X-Ray Dataset. When trained and tested on 10-fold cross-validation, the model achieved an average dice coefficient of 95.01%, intersection over union of 90.6%, and pixel accuracy of 98.82%. These scores surpass those of all other networks implemented on the same dataset. By leveraging the Attention U-Net architecture, our research showcases the potential of advanced AI techniques in dental radiography. The findings contribute to the ongoing efforts in developing automated systems that can assist dental professionals in their clinical practice.