BDU‐net: Toward accurate segmentation of dental image using border guidance and feature map distortion

Abstract

The importance of instance segmentation for each tooth is increasing in dental disease diagnosis and computer-assisted treatment. However, most existing segmentation methods are mainly concerned with semantic feature extraction, while ignoring complex situations such as blurred boundaries and dislocation of teeth in panoramic radiographs. To address these problems, we propose a dual subnetworks structure based on border guidance and feature map distortion, called BDU-net. Specifically, we embed the Disout method with the capability to distort features into the encoder to obtain multi-scale feature maps with excellent generalization. Then, the distorted feature map is submitted to two subnetworks, each having a feature pyramid structure in a shared manner, including a border subnetwork that adjusts the segmentation boundaries and a region subnetwork that generates the region segmentation results. Experiments conducted on the teeth segmentation dataset show that the proposed BDU-net performs better than the other approaches, especially for the case of teeth missing or dislocated teeth.

Artificial intelligence in the diagnosis of dental diseases on panoramic radiographs: a preliminary study

“…nnU-Net can automatically adapt to any dataset by adjusting the hyperparameters according to the data characteristics [ 35 ]. BDU-Net focuses on enhanced generalization capabilities and instance boundary adjustment, improving not only the accuracy of tooth position identification, but also achieving more accurate segmentation results for teeth boundaries [ 25 ].…”

“…In our previous study, we proposed a dual subnetworks structure based on border guidance and feature map distortion, called BDU-Net [ 25 ]. It showed great potential on improving the performance of teeth instance segmentation.…”