Accurate and Automatic Dental Crown Components Segmentation With Multi-Scale Attention Based U-Net and Hybrid Level Set Models

This paper presents a two-step method to automatically and accurately segment the dental crown components from CT images. Firstly, a multi-scale attention based U-Net model is proposed for pulp segmentation, which is embedded with global and local attention modules. The constructed attention modules can automatically aggregate pixel-wise contextual information and focus on catching the real dental pulp region. Secondly, two efficient level set models are proposed: one is the shape constraint-based level set model for enamel and dentin segmentation, the other is the region mutual exclusion-based level set model for neighboring teeth segmentation. The proposed shape constraint term can better handle topology changes of teeth and the region mutual exclusion term can more effectively avoid intersecting segmentation. Besides, a starting slice initialization method is introduced to achieve automatic segmentation, and an accurate contour propagation strategy is developed for slice-by-slice segmentation. We set up a series of comparative experiments for evaluation. Experimental results verify that the proposed method obtains promising performance for each crown component segmentation, and outperforms state-of-the-art tooth segmentation methods in terms of accuracy. This suggests that the proposed method can be used to accurately segment the crown components for precise tooth preparation treatment. Note to Practitioners—The motivation of this work is to reduce the burden on dentists during tooth preparation treatment, which requires accurate segmentation of crown components (i.e., enamel, dentin, and pulp) from dental CT images. Existing methods only focused on the segmentation of teeth or alveolar bone. Therefore, we present a novel automatic segmentation model for the dental crown components with high accuracy. A key strength of this study is the combination of a data-driven method (deep learning) and model-driven methods (level-set), which can provide good accuracy under limited training samples. This ability is highly desirable for practitioners by saving labor-intensive, costly labeling efforts. Furthermore, our proposed method will provide tools to help reduce subjectivity and human errors, as well as streamline and expedite the clinical workflow. This will significantly facilitate tooth preparation automation.

I. Introduction

Dental hard tissue disease is one of the most common oral diseases and tooth preparation is a fundamental part of its treatment process [1]. Tooth preparation refers to the operation of cutting and shaping the hard tissues of diseased teeth (including enamel and dentin) in three dimensions quantitatively in the patient’s narrow mouth using specialized high-speed grinding instruments. The quality of tooth preparation is significant for the treatment of many oral diseases. Furthermore, since the appropriate parameters for the surgical instruments to cut enamel or dentin are different, the components of the crown (i.e., enamel, dentin, and pulp) need to be accurately segmented to achieve a precise tooth preparation. With the development of medical technology, cone beam computed tomography (CBCT) has been widely applied in the diagnosis and treatment of oral diseases [2], [3]. As a consequence, it is essential to develop an automatic model to achieve accurate segmentation for the crown components from dental CT images.