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

2023/09/30 by Pengzhou Cai, Cai, Pengzhou, Jiang, Lu +2
Biochemistry, Genetics and Molecular Biology · Medicine · #Cleft Lip and Palate Research #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Fetal and Pediatric Neurological Disorders #Image and Video Processing (eess.IV) #Urological Disorders and Treatments #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2310.00289

openalex publication_date 2023/09/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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