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Pubic Symphysis-Fetal Head Segmentation Network Using BiFormer Attention Mechanism and Multipath Dilated Convolution

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MultiMedia Modeling (MMM 2025)

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Abstract

Pubic symphysis-fetal head segmentation in transperineal ultrasound images plays a critical role for the assessment of fetal head descent and progression. Existing transformer segmentation methods based on sparse attention mechanism use handcrafted static patterns, which leads to great differences in terms of segmentation performance on specific datasets. To address this issue, we introduce a dynamic, query-aware sparse attention mechanism for ultrasound image segmentation. Specifically, we propose a novel method, named BRAU-Net to solve the pubic symphysis-fetal head segmentation task in this paper. The method adopts a U-Net-like encoder-decoder architecture with bi-level routing attention and skip connections, which effectively learns local-global semantic information. In addition, we propose an inverted bottleneck patch expanding (IBPE) module to reduce information loss while performing up-sampling operations. The proposed BRAU-Net is evaluated on FH-PS-AoP and HC18 datasets. The results demonstrate that our method could achieve excellent segmentation results. The code is available on GitHub.

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Notes

  1. 1.

    https://ps-fh-aop-2023.grand-challenge.org/.

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Acknowledgments

This work is supported in part by the Scientific Research Foundation of Chongqing University of Technology under Grants 0103210650 and 0121230235, and in part by the Youth Project of Science and Technology Research Program of Chongqing Education Commission of China under Grants KJQN202301145 and KJQN202301162. We would like to thank the anonymous reviewers for their helpful comments which have led to many improvements in this paper.

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Correspondence to Libin Lan .

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Cai, P., Jiang, L., Li, Y., Liu, X., Lan, L. (2025). Pubic Symphysis-Fetal Head Segmentation Network Using BiFormer Attention Mechanism and Multipath Dilated Convolution. In: Ide, I., et al. MultiMedia Modeling. MMM 2025. Lecture Notes in Computer Science, vol 15522. Springer, Singapore. https://doi.org/10.1007/978-981-96-2064-7_18

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  • DOI: https://doi.org/10.1007/978-981-96-2064-7_18

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