DBT Masses Automatic Segmentation Using U-Net Neural Networks
DBT Masses Automatic Segmentation Using U-Net Neural Networks
复制标题
DBT 使用 U-Net 神经网络进行大规模自动分割
DOI：
10.1155/2020/7156165
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发表时间：
2020-01-28
影响因子：
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通讯作者：
Li, Ruipeng
中科院分区：
文献类型：
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作者：
Lai, Xiaobo;Yang, Weiji;Li, Ruipeng
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，提出了一种基于U-Net结构的DBT肿块自动分割算法。首先对DBT图像进行礼帽变换后，构造约束矩阵并与DBT图像相乘，以抑制背景组织噪声，增强候选区域的对比度。其次，建立一个高效的U-Net神经网络，并在数据增强之前提取图像块，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，首先将每个像素分为两种不同类型的标签。最后，所有小于50个体素的区域被认为是假阳性被删除，中值滤波器平滑的质量边界，以获得最终的分割结果。该方法可以有效地提高DBT图像中肿块的自动分割性能。以检测准确度（Acc）、灵敏度（Sen）、特异性（Spe）和曲线下面积（AUC）作为评价指标，整个实验数据集中DBT质量分割的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令人满意的结果，这是上级优于一些经典的架构，并可能有望具有临床应用前景。
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.