Ultrasound image segmentation plays an important role in computer-aided diagnosis of breast cancer. Existing approaches focused on extracting the tumor tissue to characterize the tumor class. However, other tissues are also helpful for providing the references. In this paper, a multi-target semantic segmentation approach is proposed based on the fully convolutional network for segmenting the breast ultrasound image into different target tissue regions. For handling the uncertain affiliation of pixels in blurry boundaries, the certain outputs of pixel characteristics in AlexNet are transformed into the fuzzy decision expression. For improving the image detail representation, the AlexNet network structure of fully convolutional network is optimized with fully connected skip structure. In addition, the output of net model is optimized with fully connected conditional random field to improve the characterization of spatial consistency and pixels’ correlation of the image. Moreover, a data training optimization method is developed for improving the efficiency of network training. In the experiment, 325 ultrasound images and four error metrics are utilized for validating the segmentation performance. Comparing with existing methods, experimental results show that the proposed approach is effective for handling the breast ultrasound images accurately and reliably.

Graphical abstract

超声图像分割在乳腺癌的计算机辅助诊断中起着重要作用。现有方法集中于提取肿瘤组织以表征肿瘤类别。但是，其他组织也有助于提供参考。本文提出了一种基于全卷积网络的多目标语义分割方法，用于将乳房超声图像分割为不同的目标组织区域。为了处理模糊边界中像素的不确定隶属关系，将AlexNet中像素特征的某些输出转换为模糊决策表达式。为了改善图像细节表示，完全卷积网络的AlexNet网络结构使用完全连接的跳过结构进行了优化。此外，完全连接的条件随机场对网络模型的输出进行了优化，以改善图像的空间一致性和像素相关性的表征。此外，开发了一种数据训练优化方法以提高网络训练的效率。在实验中，利用325张超声图像和四个误差指标来验证分割性能。与现有方法相比，实验结果表明，该方法对于准确，可靠地处理乳房超声图像是有效的。使用325张超声图像和四个错误度量标准来验证分割性能。与现有方法相比，实验结果表明，该方法对于准确，可靠地处理乳房超声图像是有效的。使用325张超声图像和四个错误度量标准来验证分割性能。与现有方法相比，实验结果表明，该方法对于准确，可靠地处理乳房超声图像是有效的。

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