MRCM-UCTransNet: Automatic and Accurate 3D Tooth Segmentation Network From Cone-Beam CT Images

Abstract

Many scenarios in dental clinical diagnosis and treatment require the segmentation and identification of a specific tooth or the entire dentition in cone-beam computed tomography (CBCT) images. However, traditional segmentation methods struggle to ensure accuracy. In recent years, there has been significant progress in segmentation algorithms based on deep learning, garnering considerable attention. Inspired by models from present neuro networks such as UCTransNet and DC-Unet, this study proposes an MRCM-UCTransNet for accurate three-dimensional tooth segmentation from cone-beam CT images. To enhance feature extraction while preserving the multi-head attention mechanism, a multi-scale residual convolution module (MRCM) is integrated into the UCTransNet architecture. This modification addresses the limitations of traditional segmentation methods and aims to improve accuracy in tooth segmentation from CBCT images. Comparative experiments indicate that, in the situation with a specific image size and small data volume, the proposed method exhibits certain advantages in segmentation accuracy and precision. Compared to traditional Unet approaches, MRCM-UCTransNet's dice accuracy is improved by 7%, while its sensitivity is improved by about 10%. These findings highlight the efficacy of the proposed approach, particularly in scenarios with specific image size constraints and limited data availability. The proposed MRCM-UCTransNet algorithm integrates the latest architectural advancements in the Unet model which achieves effective segmentation of six types of teeth within the tooth. It was proved to be efficient for image segmentation on small datasets, requiring less training time and fewer parameters.