T-Mamba: Frequency-Enhanced Gated Long-Range Dependency for Tooth 3D CBCT Segmentation

arXiv:2404.01065v1 Announce Type: new
Abstract: 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 …

arxiv cs.cv mamba segmentation type