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

• URL: http://arxiv.org/abs/2310.00289v3
• Date: Thu, 14 Nov 2024 02:08:40 GMT
• Title: Pubic Symphysis-Fetal Head Segmentation Using Pure Transformer with Bi-level Routing Attention
• Authors: Pengzhou Cai, Lu Jiang, Yanxin Li, Libin Lan,
• Abstract summary: 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.
• Score: 6.709399356217316
• License:
• 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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