mlsanityData to 14 Oct 2026

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

arXiv 2410.10352 ↗First posted 14 Oct 2024eess.IV cs.CVcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 16 of its 25 references that are in the map. First posted on arXiv 14 Oct 2024.

Builds on

16 of 25 references.

References · 16

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  4. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  5. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  6. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  7. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
  8. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
  9. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,222
  10. 2021Medical Transformer: Gated Axial-Attention for Medical Image Segmentationcited by 320
  11. 2021TransFuse: Fusing Transformers and CNNs for Medical Image Segmentationcited by 258
  12. 2023BiFormer: Vision Transformer with Bi-Level Routing Attentioncited by 151
4 more
  1. 2022HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentationcited by 95
  2. 2021TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentationcited by 60
  3. 2022Contextual Attention Network: Transformer Meets U-Netcited by 33
  4. 2022Rethinking the Unpretentious U-net for Medical Ultrasound Image Segmentationcited by 13

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Citing papers per year: 2025: 3.

3 papers.

  1. 2025MedFormer: Hierarchical Medical Vision Transformer with Content-Aware Dual Sparse Selection Attentioncited by 3
  2. 2025MSLAU-Net: A Hybrid CNN-Transformer Network for Medical Image Segmentationcited by 0
  3. 2025TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentationcited by 0