TY  - JOUR
AU  - Su, Tingli
AU  - Wan, Rui
AU  - Wang, Senmao
AU  - Bai, Yuting
PY  - 2026
DA  - 2026/03/06
TI  - SEFF-Net: A Hybrid Feature Fusion Network for Accurate Segmentation of Breast Ultrasound Images
JO  - ICCK Transactions on Emerging Topics in Artificial Intelligence
T2  - ICCK Transactions on Emerging Topics in Artificial Intelligence
JF  - ICCK Transactions on Emerging Topics in Artificial Intelligence
VL  - 3
IS  - 2
SP  - 128
EP  - 141
DO  - 10.62762/TETAI.2026.494190
UR  - https://www.icck.org/article/abs/TETAI.2026.494190
KW  - breast ultrasound segmentation
KW  - edge enhancement
KW  - feature fusion
KW  - hybrid loss strategy
KW  - image processing
AB  - Breast ultrasound imaging plays a crucial role in early breast cancer screening and diagnosis due to its noninvasive nature and cost-effectiveness. However, accurate lesion segmentation remains challenging because of severe speckle noise, low contrast, and blurred tumor boundaries. To address these issues, this paper proposes SEFF-Net, a novel edge-aware feature fusion network with a U-shaped encoder–decoder architecture to capture multi-level semantic representations for breast ultrasound image segmentation task. To enhance boundary perception, a Self-learning Edge Enhancement Module is embedded in the shallow encoding stages, while a Spatial Feature Fusion Module is introduced to effectively integrate multi-scale features by leveraging spatial context, thereby achieving a better balance between low-level spatial details and high-level semantic information.To further alleviate the class imbalance between foreground and background regions and improve boundary learning, a novel joint loss function is designed by combining region-based consistency constraints with boundary-sensitive supervision. This optimization strategy reinforces contour awareness while maintaining overall segmentation accuracy. Experimental results demonstrate that SEFF-Net consistently outperforms state-of-the-art segmentation methods across multiple evaluation metrics, including Dice coefficient, IoU, and boundary-related measures. Overall, SEFF-Net provides an effective and reliable solution for accurate breast ultrasound image segmentation, showing promising potential for clinical computer-aided diagnosis systems.
SN  - 3068-6652
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Su2026SEFFNet,
  author = {Tingli Su and Rui Wan and Senmao Wang and Yuting Bai},
  title = {SEFF-Net: A Hybrid Feature Fusion Network for Accurate Segmentation of Breast Ultrasound Images},
  journal = {ICCK Transactions on Emerging Topics in Artificial Intelligence},
  year = {2026},
  volume = {3},
  number = {2},
  pages = {128-141},
  doi = {10.62762/TETAI.2026.494190},
  url = {https://www.icck.org/article/abs/TETAI.2026.494190},
  abstract = {Breast ultrasound imaging plays a crucial role in early breast cancer screening and diagnosis due to its noninvasive nature and cost-effectiveness. However, accurate lesion segmentation remains challenging because of severe speckle noise, low contrast, and blurred tumor boundaries. To address these issues, this paper proposes SEFF-Net, a novel edge-aware feature fusion network with a U-shaped encoder–decoder architecture to capture multi-level semantic representations for breast ultrasound image segmentation task. To enhance boundary perception, a Self-learning Edge Enhancement Module is embedded in the shallow encoding stages, while a Spatial Feature Fusion Module is introduced to effectively integrate multi-scale features by leveraging spatial context, thereby achieving a better balance between low-level spatial details and high-level semantic information.To further alleviate the class imbalance between foreground and background regions and improve boundary learning, a novel joint loss function is designed by combining region-based consistency constraints with boundary-sensitive supervision. This optimization strategy reinforces contour awareness while maintaining overall segmentation accuracy. Experimental results demonstrate that SEFF-Net consistently outperforms state-of-the-art segmentation methods across multiple evaluation metrics, including Dice coefficient, IoU, and boundary-related measures. Overall, SEFF-Net provides an effective and reliable solution for accurate breast ultrasound image segmentation, showing promising potential for clinical computer-aided diagnosis systems.},
  keywords = {breast ultrasound segmentation, edge enhancement, feature fusion, hybrid loss strategy, image processing},
  issn = {3068-6652},
  publisher = {Institute of Central Computation and Knowledge}
}