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

arXiv: https://arxiv.org/abs/2310.00289 · HTML: https://mlsanity.com/paper/2310.00289/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 30 Sep 2023.

## Builds on (most cited first, 3 of 3)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [BiFormer: Vision Transformer with Bi-Level Routing Attention](https://mlsanity.com/paper/2303.08810/) (2023)

## Built on by (most cited first, 2 of 2)

- [BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation](https://mlsanity.com/paper/2401.00722/) (2024)
- [DSSAU-Net:U-Shaped Hybrid Network for Pubic Symphysis and Fetal Head Segmentation](https://mlsanity.com/paper/2506.03684/) (2025)

## Abstract (arXiv)

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.