mlsanityData to 14 Oct 2026

DBF-Net: A Dual-Branch Network with Feature Fusion for Ultrasound Image Segmentation

arXiv 2411.11116 ↗First posted 17 Nov 2024eess.IV cs.CVcited by 0

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Abstract (arXiv)

Accurately segmenting lesions in ultrasound images is challenging due to the difficulty in distinguishing boundaries between lesions and surrounding tissues. While deep learning has improved segmentation accuracy, there is limited focus on boundary quality and its relationship with body structures. To address this, we introduce UBBS-Net, a dual-branch deep neural network that learns the relationship between body and boundary for improved segmentation. We also propose a feature fusion module to integrate body and boundary information. Evaluated on three public datasets, UBBS-Net outperforms existing methods, achieving Dice Similarity Coefficients of 81.05% for breast cancer, 76.41% for brachial plexus nerves, and 87.75% for infantile hemangioma segmentation. Our results demonstrate the effectiveness of UBBS-Net for ultrasound image segmentation. The code is available at https://github.com/apple1986/DBF-Net.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 43 references that are in the map. First posted on arXiv 17 Nov 2024.

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19 of 43 references.

References · 19

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7 more
  1. 2019ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentationcited by 38
  2. 2019Efficient Segmentation: Learning Downsampling Near Semantic Boundariescited by 33
  3. 2021A Battle of Network Structures: An Empirical Study of CNN, Transformer, and MLPcited by 32
  4. 2022Rethinking the Unpretentious U-net for Medical Ultrasound Image Segmentationcited by 13
  5. 2020Learning Directional Feature Maps for Cardiac MRI Segmentationcited by 10
  6. 2020Deep Learning in Medical Ultrasound Image Segmentation: a Reviewcited by 8
  7. 2018Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networkscited by 5

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