T-Mamba: A unified framework with Long-Range Dependency in dual-domain for 2D & 3D Tooth Segmentation

Tooth segmentation is an essential step in digital dental diagnosis and treatment planning, playing a crucial role across various dental specialties. Despite its importance, this process is fraught with challenges due to the high noise and low contrast inherent in 2D and 3D dental radiographic images. Both convolutional neural networks (CNNs) and transformers have shown promise in tooth image segmentation, yet each method has limitations in handling long-range dependencies and computational complexity. To address this issue, this paper introduces T-Mamba, integrating frequency-based features and shared bi-positional encoding into vision mamba to address limitations in modeling global features efficiently. Besides, we designed a gate selection unit to integrate two features in the spatial domain and one feature in the frequency domain adaptively. T-Mamba is the first to integrate frequency-based features into vision mamba, offering the flexibility to process both 2D and 3D tooth image data without the need for separate modules. Additionally, the TED, a large-scale public 2D dental X-ray dataset, has been presented in this paper. Extensive experiments demonstrated that T-Mamba achieved new SOTA performance on both the 3D dental CBCT dataset and our proposed 2D TED dataset.