  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.
