MPCNet: Improved MeshSegNet Based on Position Encoding and Channel Attention

Abstract

In the process of orthodontic treatment, it is a very important step to accurately segment each tooth and jaw model with computer assistance. The use of deep learning technology methods for tooth segmentation can not only save a lot of manual interaction and time cost but also improve the treatment effect. 3D tooth segmentation is a hot topic of interest for international related scholars, and some end-to-end tooth segmentation methods based on dental mesh scanning models have been emerging in recent years. Due to the limited variety of existing models, they are not well suited for different 3D segmentation scenarios, and the feature extraction capability and segmentation effect of these models still need to be improved. In this paper, we propose a novel end-to-end tooth segmentation method, MPCNet, which adds multi-scale mesh density information to the input layer, uses position encoding and channel attention mechanism to improve MeshSegNet, and uses graph-cut post-processing to perform 3D tooth segmentation in real scenes. The effectiveness of MPCNet is evaluated on a real 3D scanned tooth segmentation dataset, which significantly outperforms the current mainstream segmentation methods.