National Digital Switching System Engineering and Technological Research Center, Zhengzhou, Henan Province, China.
Department of Radiology, Henan Provincial People's Hospital, Zhengzhou, Henan Province, China.
J Healthc Eng. 2019 Jan 14;2019:8415485. doi: 10.1155/2019/8415485. eCollection 2019.
Breast tumor segmentation plays a crucial role in subsequent disease diagnosis, and most algorithms need interactive prior to firstly locate tumors and perform segmentation based on tumor-centric candidates. In this paper, we propose a fully convolutional network to achieve automatic segmentation of breast tumor in an end-to-end manner. Considering the diversity of shape and size for malignant tumors in the digital mammograms, we introduce multiscale image information into the fully convolutional dense network architecture to improve the segmentation precision. Multiple sampling rates of atrous convolution are concatenated to acquire different field-of-views of image features without adding additional number of parameters to avoid over fitting. Weighted loss function is also employed during training according to the proportion of the tumor pixels in the entire image, in order to weaken unbalanced classes problem. Qualitative and quantitative comparisons demonstrate that the proposed algorithm can achieve automatic tumor segmentation and has high segmentation precision for various size and shapes of tumor images without preprocessing and postprocessing.
乳腺肿瘤分割在后续疾病诊断中起着至关重要的作用，大多数算法需要在首先定位肿瘤并基于以肿瘤为中心的候选物进行分割之前进行交互。在本文中，我们提出了一种完全卷积网络，以端到端的方式实现乳腺肿瘤的自动分割。考虑到数字乳腺片中恶性肿瘤的形状和大小的多样性，我们将多尺度图像信息引入到完全卷积密集网络架构中，以提高分割精度。多个空洞卷积的采样率被连接起来，以获取图像特征的不同视场，而不会增加额外的参数来避免过拟合。在训练过程中还根据整个图像中肿瘤像素的比例使用加权损失函数，以减弱不平衡类问题。定性和定量比较表明，所提出的算法可以实现自动肿瘤分割，并且对于各种大小和形状的肿瘤图像具有较高的分割精度，无需预处理和后处理。
