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.

FDNet 是一种特征解耦分割网络，通过使用 LF-Wavelet 来强调患者牙齿的全局结构完整性，以及使用 SAM 编码器来改善边界描绘，从而提高相邻牙齿结构之间的对比度，解决了 CBCT 扫描中复杂的伪影和不清晰牙齿边界的问题，并在严格的基准测试中验证了其有效性，达到了 85.28% 和 75.23% 的顶级 Dice 和 IoU 分数。这种创新的语义和边界特征解耦方式充分发挥了各个元素的独特优势，显著提高了分割性能的质量。