  In this paper, we propose a method, named BRAU-Net, to solve the pubic
symphysis-fetal head segmentation task. The method adopts a U-Net-like pure
Transformer architecture with bi-level routing attention and skip connections,
which effectively learns local-global semantic information. The proposed
BRAU-Net was evaluated on transperineal Ultrasound images dataset from the
pubic symphysis-fetal head segmentation and angle of progression (FH-PS-AOP)
challenge. The results demonstrate that the proposed BRAU-Net achieves
comparable a final score. The codes will be available at
https://github.com/Caipengzhou/BRAU-Net.
