Abstract
Accurate and automatic segmentation of individual tooth is critical for computer-aided analysis towards clinical decision support and treatment planning. Three-dimensional reconstruction of individual tooth after the segmentation also plays an important role in simulation in digital orthodontics. However, it is difficult to automatically segment individual tooth in cone beam computed tomography (CBCT) images due to the blurring boundaries of neighboring teeth and the similar intensities between teeth and mandible bone. In this work, we propose the use of a multi-task 3D fully convolutional network (FCN) and marker-controlled watershed transform (MWT) to segment individual tooth. The multi-task FCN learns to simultaneously predict the probability of tooth region and the probability of tooth surface. Through the combination of the tooth probability gradient map and the surface probability map as the input image, MWT is used to automatically separate and segment individual tooth. Twenty-five dental CBCT scans are used in the study. The average Dice similarity coefficient, Jaccard index, and relative volume difference are 0.936 (±0.012), 0.881 (±0.019), and 0.072 (±0.027), respectively, and the average symmetric surface distance is 0.363 (±0.145) mm for our method. The experimental results demonstrate that the multi-task 3D FCN combined with MWT can segment individual tooth of various types in dental CBCT images.
Highlights
Dental cone beam computed tomography (CBCT), a diagnostic imaging technique, is widely used for dental diseases and dental problems researching [1]
Clinical dental CBCT images of 25 patients were randomly collected by Stomatological Hospital of Southern Medical University, Guangzhou, China
The predicted maps were used for individual tooth segmentation by marker-controlled watershed transform (MWT)
Summary
Dental cone beam computed tomography (CBCT), a diagnostic imaging technique, is widely used for dental diseases and dental problems researching [1]. To address these difficulties, many tooth segmentation methods for dental CBCT images have been proposed. Many tooth segmentation methods for dental CBCT images have been proposed These methods can be divided into two categories: conventional methods which require handcrafted features, and deep learning methods which often need a lot of samples. Evain et al [2] used graph cut methods to segment individual tooth from dental CBCT images and achieved a high Dice score.
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