Dual Attention Residual U-Net for Accurate Brain Ultrasound Segmentation in IVH Detection

• URL: http://arxiv.org/abs/2505.17683v2
• Date: Tue, 10 Jun 2025 04:20:37 GMT
• Title: Dual Attention Residual U-Net for Accurate Brain Ultrasound Segmentation in IVH Detection
• Authors: Dan Yuan, Yi Feng, Ziyun Tang,
• Abstract summary: Intraventricular hemorrhage (IVH) is a severe neurological complication among premature infants.<n>Recent deep learning methods offer promise for computer-aided diagnosis.<n>We propose an enhanced Residual U-Net architecture incorporating two complementary attention mechanisms.
• Score: 5.77500692308611
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intraventricular hemorrhage (IVH) is a severe neurological complication among premature infants, necessitating early and accurate detection from brain ultrasound (US) images to improve clinical outcomes. While recent deep learning methods offer promise for computer-aided diagnosis, challenges remain in capturing both local spatial details and global contextual dependencies critical for segmenting brain anatomies. In this work, we propose an enhanced Residual U-Net architecture incorporating two complementary attention mechanisms: the Convolutional Block Attention Module (CBAM) and a Sparse Attention Layer (SAL). The CBAM improves the model's ability to refine spatial and channel-wise features, while the SAL introduces a dual-branch design, sparse attention filters out low-confidence query-key pairs to suppress noise, and dense attention ensures comprehensive information propagation. Extensive experiments on the Brain US dataset demonstrate that our method achieves state-of-the-art segmentation performance, with a Dice score of 89.04% and IoU of 81.84% for ventricle region segmentation. These results highlight the effectiveness of integrating spatial refinement and attention sparsity for robust brain anatomy detection. Code is available at: https://github.com/DanYuan001/BrainImgSegment.

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