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。

FDNet：牙齿 CBCT 图像的特征解耦分割网络

FDNet: Feature Decoupled Segmentation Network for Tooth CBCT Image

This paper presents FDNet: a Focal Decomposed Network for efficient, robust
and practical time series forecasting. We break away from conventional deep
time series forecasting formulas which obtain prediction results from universal
feature maps of input sequences. In contrary, FDNet neglects universal
correlations of input elements and only extracts fine-grained local features
from input sequence. We show that: (1) Deep time series forecasting with only
fine-grained local feature maps of input sequence is feasible upon theoretical
basis. (2) By abandoning global coarse-grained feature maps, FDNet overcomes
distribution shift problem caused by changing dynamics of time series which is
common in real-world applications. (3) FDNet is not dependent on any inductive
bias of time series except basic auto-regression, making it general and
practical. Moreover, we propose focal input sequence decomposition method which
decomposes input sequence in a focal manner for efficient and robust
forecasting when facing Long Sequence Time series Input (LSTI) problem. FDNet
achieves competitive forecasting performances on six real-world benchmarks and
reduces prediction MSE by 38.4% on average compared with other thirteen SOTA
baselines. The source code is available at this https URL

本研究提出了 FDNet，一个面向高效、鲁棒、实际的时序预测模型，利用局部特征图从输入序列中提取精细的局部特征，实现了在没有全局粗略特征图的情况下的深度时序预测，并通过焦点输入序列分解方法解决了长序列输入问题。在六项现实世界基准测试中，FDNet 取得了竞争性的预测结果，并平均减少了 38.4％的预测 MSE 与其他 13 个基线相比。