Papers › GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation

GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation

30 Sep 2021arXiv:2109.14813archive 2025-07-28

Yunxiang Li, Shuai Wang, Jun Wang, Guodong Zeng, Wenjun Liu, Qianni Zhang, Qun Jin, Yaqi Wang

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.

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Anatomy Segmentation

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Absolute Position Encodings Adam Attention BPE Concatenated Skip Connection Convolution Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Max Pooling Multi-Head Attention Position-Wise Feed-Forward Layer ReLU Residual Connection Softmax Transformer U-Net

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