College of Medical Technology, Zhejiang Chinese Medical University, Hangzhou 310053, China.
College of Life Science, Zhejiang Chinese Medical University, Hangzhou 310053, China.
Comput Math Methods Med. 2020 Jan 28;2020:7156165. doi: 10.1155/2020/7156165. eCollection 2020.
To improve the automatic segmentation accuracy of breast masses in digital breast tomosynthesis (DBT) images, we propose a DBT mass automatic segmentation algorithm by using a U-Net architecture. Firstly, to suppress the background tissue noise and enhance the contrast of the mass candidate regions, after the top-hat transform of DBT images, a constraint matrix is constructed and multiplied with the DBT image. Secondly, an efficient U-Net neural network is built and image patches are extracted before data augmentation to establish the training dataset to train the U-Net model. And then the presegmentation of the DBT tumors is implemented, which initially classifies per pixel into two different types of labels. Finally, all regions smaller than 50 voxels considered as false positives are removed, and the median filter smoothes the mass boundaries to obtain the final segmentation results. The proposed method can effectively improve the performance in the automatic segmentation of the masses in DBT images. Using the detection Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), and area under the curve (AUC) as evaluation indexes, the Acc, Sen, Spe, and AUC for DBT mass segmentation in the entire experimental dataset is 0.871, 0.869, 0.882, and 0.859, respectively. Our proposed U-Net-based DBT mass automatic segmentation system obtains promising results, which is superior to some classical architectures, and may be expected to have clinical application prospects.
为了提高数字乳腺断层合成（DBT）图像中乳腺肿块的自动分割精度，我们提出了一种基于 U-Net 架构的 DBT 肿块自动分割算法。首先，通过对 DBT 图像进行顶帽变换，抑制背景组织噪声，增强肿块候选区域的对比度，构建约束矩阵并与 DBT 图像相乘。其次，构建一个高效的 U-Net 神经网络，并在数据增强之前提取图像补丁，以建立训练数据集来训练 U-Net 模型。然后对 DBT 肿瘤进行预分割，初步将每个像素分为两种不同类型的标签。最后，去除所有小于 50 体素的区域作为假阳性，并通过中值滤波器平滑肿块边界，得到最终的分割结果。该方法可以有效地提高 DBT 图像中肿块自动分割的性能。使用检测准确率（Acc）、灵敏度（Sen）、特异性（Spe）和曲线下面积（AUC）作为评价指标，整个实验数据集的 DBT 肿块分割的 Acc、Sen、Spe 和 AUC 分别为 0.871、0.869、0.882 和 0.859。我们提出的基于 U-Net 的 DBT 肿块自动分割系统取得了有前景的结果，优于一些经典的架构，有望具有临床应用前景。
