T-Mamba: Frequency-Enhanced Gated Long-Range Dependency for Tooth 3D
  CBCT Segmentation
Jing Hao Lei He Kuo Feng Hung
Efficient tooth segmentation in three-dimensional (3D) imaging, critical for
orthodontic diagnosis, remains challenging due to noise, low contrast, and
artifacts in CBCT images. Both convolutional Neural Networks (CNNs) and
transformers have emerged as popular architectures for image segmentation.
However, their efficacy in handling long-range dependencies is limited due to
inherent locality or computational complexity. To address this issue, we
propose T-Mamba, integrating shared positional encoding and frequency-based
features into vision mamba, to address limitations in spatial position
preservation and feature enhancement in frequency domain. Besides, we also
design a gate selection unit to integrate two features in spatial domain and
one feature in frequency domain adaptively. T-Mamba is the first work to
introduce frequency-based features into vision mamba. Extensive experiments
demonstrate that T-Mamba achieves new SOTA results on the public Tooth CBCT
dataset and outperforms previous SOTA methods by a large margin, i.e., IoU +
3.63%, SO + 2.43%, DSC +2.30%, HD -4.39mm, and ASSD -0.37mm. The code and
models are publicly available at https://github.com/isbrycee/T-Mamba.