Xin Junchang, Yu Yaqi, Shen Qi, Zhang Shudi, Su Na, Wang Zhiqiong
School of Computer Science and Engineering, Northeastern University, Shenyang, 110169, China.
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China.
Med Biol Eng Comput. 2025 Jun;63(6):1809-1820. doi: 10.1007/s11517-025-03304-2. Epub 2025 Jan 30.
Accurately and swiftly segmenting breast tumors is significant for cancer diagnosis and treatment. Ultrasound imaging stands as one of the widely employed methods in clinical practice. However, due to challenges such as low contrast, blurred boundaries, and prevalent shadows in ultrasound images, tumor segmentation remains a daunting task. In this study, we propose BCT-Net, a network amalgamating CNN and transformer components for breast tumor segmentation. BCT-Net integrates a dual-level attention mechanism to capture more features and redefines the skip connection module. We introduce the utilization of a classification task as an auxiliary task to impart additional semantic information to the segmentation network, employing supervised contrastive learning. A hybrid objective loss function is proposed, which combines pixel-wise cross-entropy, binary cross-entropy, and supervised contrastive learning loss. Experimental results demonstrate that BCT-Net achieves high precision, with Pre and DSC indices of 86.12% and 88.70%, respectively. Experiments conducted on the BUSI dataset of breast ultrasound images manifest that this approach exhibits high accuracy in breast tumor segmentation.
准确且快速地分割乳腺肿瘤对于癌症诊断和治疗具有重要意义。超声成像作为临床实践中广泛应用的方法之一。然而，由于超声图像存在对比度低、边界模糊和阴影普遍等挑战，肿瘤分割仍然是一项艰巨的任务。在本研究中，我们提出了BCT-Net，一种融合了CNN和Transformer组件用于乳腺肿瘤分割的网络。BCT-Net集成了双级注意力机制以捕获更多特征，并重新定义了跳跃连接模块。我们引入将分类任务作为辅助任务，通过监督对比学习为分割网络赋予额外的语义信息。提出了一种混合目标损失函数，它结合了逐像素交叉熵、二元交叉熵和监督对比学习损失。实验结果表明，BCT-Net实现了高精度，其Precision和DSC指标分别为86.12%和88.70%。在乳腺超声图像的BUSI数据集上进行的实验表明，该方法在乳腺肿瘤分割中表现出高准确率。
