Boundary feature fusion network for tooth image segmentation

TL;DR

This paper presents a novel boundary feature fusion network for tooth image segmentation that effectively addresses blurred boundary issues, achieving high accuracy and outperforming existing methods in challenging dental image datasets.

Contribution

It introduces a boundary feature extraction module and a feature cross-fusion module to enhance boundary delineation in tooth segmentation models.

Findings

Achieved a score of 0.91 in the STS Data Challenge.

Outperformed existing tooth segmentation methods.

Effectively handled blurred boundary issues.

Abstract

Tooth segmentation is a critical technology in the field of medical image segmentation, with applications ranging from orthodontic treatment to human body identification and dental pathology assessment. Despite the development of numerous tooth image segmentation models by researchers, a common shortcoming is the failure to account for the challenges of blurred tooth boundaries. Dental diagnostics require precise delineation of tooth boundaries. This paper introduces an innovative tooth segmentation network that integrates boundary information to address the issue of indistinct boundaries between teeth and adjacent tissues. This network's core is its boundary feature extraction module, which is designed to extract detailed boundary information from high-level features. Concurrently, the feature cross-fusion module merges detailed boundary and global semantic information in a synergistic way, allowing for stepwise layer transfer of feature information. This method results in precise tooth segmentation. In the most recent STS Data Challenge, our methodology was rigorously tested and received a commendable overall score of 0.91. When compared to other existing approaches, this score demonstrates our method's significant superiority in segmenting tooth boundaries.