FDNet: Feature Decoupled Segmentation Network for Tooth CBCT Image

TL;DR

FDNet is a novel segmentation network that combines wavelet-based global structure enhancement with boundary refinement to improve the accuracy of tooth segmentation in CBCT images, addressing artifacts and indistinct boundaries.

Contribution

The paper introduces FDNet, which decouples semantic and boundary features using LF-Wavelet and SAM encoder, achieving state-of-the-art segmentation performance on CBCT datasets.

Findings

Achieved top Dice score of 85.28% and IoU of 75.23%.

Effectively handles artifacts and indistinct boundaries in CBCT images.

Outperforms existing methods in tooth segmentation accuracy.

Abstract

Precise Tooth Cone Beam Computed Tomography (CBCT) image segmentation is crucial for orthodontic treatment planning. In this paper, we propose FDNet, a Feature Decoupled Segmentation Network, to excel in the face of the variable dental conditions encountered in CBCT scans, such as complex artifacts and indistinct tooth boundaries. The Low-Frequency Wavelet Transform (LF-Wavelet) is employed to enrich the semantic content by emphasizing the global structural integrity of the teeth, while the SAM encoder is leveraged to refine the boundary delineation, thus improving the contrast between adjacent dental structures. By integrating these dual aspects, FDNet adeptly addresses the semantic gap, providing a detailed and accurate segmentation. The framework's effectiveness is validated through rigorous benchmarks, achieving the top Dice and IoU scores of 85.28% and 75.23%, respectively. This innovative decoupling of semantic and boundary features capitalizes on the unique strengths of each element to elevate the quality of segmentation performance.