Pubic Symphysis-Fetal Head Segmentation Using Pure Transformer with Bi-level Routing Attention

Pengzhou Cai, Lu Jiang, Yanxin Li, Libin Lan
9/30/2023
eess.IVcs.CV

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Abstract

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.

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Cite this paper

@article{cai2023pubic,
  title  = {Pubic Symphysis-Fetal Head Segmentation Using Pure Transformer with Bi-level Routing Attention},
  author = {Pengzhou Cai and Lu Jiang and Yanxin Li and Libin Lan},
  year   = {2023},
  eprint = {2310.00289},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.00289v3}
}

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