Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Department of Electronic Engineering, Shantou University, Shantou, Guangdong, China.
Department of Electronics and Communication Engineering, BMS Institute of Technology and Management, Bengaluru, Karnataka, India.
PLoS One. 2019 Aug 23;14(8):e0221535. doi: 10.1371/journal.pone.0221535. eCollection 2019.
Breast cancer is a common gynecological disease that poses a great threat to women health due to its high malignant rate. Breast cancer screening tests are used to find any warning signs or symptoms for early detection and currently, Ultrasound screening is the preferred method for breast cancer diagnosis. The localization and segmentation of the lesions in breast ultrasound (BUS) images are helpful for clinical diagnosis of the disease. In this paper, an RDAU-NET (Residual-Dilated-Attention-Gate-UNet) model is proposed and employed to segment the tumors in BUS images. The model is based on the conventional U-Net, but the plain neural units are replaced with residual units to enhance the edge information and overcome the network performance degradation problem associated with deep networks. To increase the receptive field and acquire more characteristic information, dilated convolutions were used to process the feature maps obtained from the encoder stages. The traditional cropping and copying between the encoder-decoder pipelines were replaced by the Attention Gate modules which enhanced the learning capabilities through suppression of background information. The model, when tested with BUS images with benign and malignant tumor presented excellent segmentation results as compared to other Deep Networks. A variety of quantitative indicators including Accuracy, Dice coefficient, AUC(Area-Under-Curve), Precision, Sensitivity, Specificity, Recall, F1score and M-IOU (Mean-Intersection-Over-Union) provided performances above 80%. The experimental results illustrate that the proposed RDAU-NET model can accurately segment breast lesions when compared to other deep learning models and thus has a good prospect for clinical diagnosis.
乳腺癌是一种常见的妇科疾病，由于其高恶性率，对女性健康构成了极大威胁。乳腺癌筛查测试用于发现任何警告信号或症状，以便进行早期检测，目前，超声筛查是乳腺癌诊断的首选方法。对乳腺超声（BUS）图像中的病变进行定位和分割有助于对疾病进行临床诊断。本文提出并采用 RDAU-NET（残差扩张注意力门 UNet）模型对 BUS 图像中的肿瘤进行分割。该模型基于传统的 U-Net，但将普通的神经单元替换为残差单元，以增强边缘信息并克服与深度网络相关的网络性能下降问题。为了增加感受野并获取更多特征信息，使用扩张卷积处理来自编码器阶段的特征图。通过注意力门模块替换了编码器-解码器管道之间的传统裁剪和复制，该模块通过抑制背景信息来增强学习能力。与良性和恶性肿瘤的 BUS 图像进行测试时，该模型的分割结果明显优于其他深度网络，具有 80%以上的准确性、Dice 系数、AUC（曲线下面积）、精度、灵敏度、特异性、召回率、F1 分数和 M-IOU（平均交集-重叠）等多种定量指标。实验结果表明，与其他深度学习模型相比，所提出的 RDAU-NET 模型可以更准确地分割乳腺病变，因此具有良好的临床诊断前景。
