DBF-Net：一种用于超声图像分割的特征融合双分支网络
DBF-Net: A Dual-Branch Network with Feature Fusion for Ultrasound Image Segmentation
AI总结：
针对超声图像病灶边界分割困难的问题，提出双分支网络UBBS-Net及特征融合模块，通过学习身体与边界关系提升分割精度，在三个数据集上优于现有方法。
摘要：
由于难以区分病灶与周围组织的边界，准确分割超声图像中的病灶具有挑战性。尽管深度学习提升了分割精度，但对边界质量及其与身体结构关系的关注有限。为此，我们提出了UBBS-Net，这是一种双分支深度神经网络，能够学习身体与边界之间的关系以改善分割效果。我们还提出了一个特征融合模块来整合身体与边界信息。在三个公开数据集上的评估表明，UBBS-Net优于现有方法，在乳腺癌、臂丛神经和婴儿血管瘤分割中分别实现了81.05%、76.41%和87.75%的Dice相似系数。我们的结果证明了UBBS-Net在超声图像分割中的有效性。代码可在https://github.com/apple1986/DBF-Net获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
