FDNet: Feature Decoupled Segmentation Network for Tooth CBCT Image
Xiang Feng Chengkai Wang Chengyu Wu Yunxiang Li Yongbo He Shuai Wang Yaiqi Wang
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 significantly elevate the quality of segmentation performance.