GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation
This work addresses a domain-specific problem for dental imaging, providing an incremental improvement in segmentation accuracy for root canal therapy assessment.
The paper tackles the challenge of tooth root segmentation in oral X-ray images, where fuzzy boundaries make it difficult, by proposing GT U-Net, a U-Net-like group Transformer network that achieves state-of-the-art performance on a collected tooth root dataset and the public DRIVE retina dataset.
To achieve an accurate assessment of root canal therapy, a fundamental step is to perform tooth root segmentation on oral X-ray images, in that the position of tooth root boundary is significant anatomy information in root canal therapy evaluation. However, the fuzzy boundary makes the tooth root segmentation very challenging. In this paper, we propose a novel end-to-end U-Net like Group Transformer Network (GT U-Net) for the tooth root segmentation. The proposed network retains the essential structure of U-Net but each of the encoders and decoders is replaced by a group Transformer, which significantly reduces the computational cost of traditional Transformer architectures by using the grouping structure and the bottleneck structure. In addition, the proposed GT U-Net is composed of a hybrid structure of convolution and Transformer, which makes it independent of pre-training weights. For optimization, we also propose a shape-sensitive Fourier Descriptor (FD) loss function to make use of shape prior knowledge. Experimental results show that our proposed network achieves the state-of-the-art performance on our collected tooth root segmentation dataset and the public retina dataset DRIVE. Code has been released at https://github.com/Kent0n-Li/GT-U-Net.