Breast cancer is one of the leading causes of death among women worldwide. Mammographic mass segmentation is an important task in mammogram analysis. This process, however, poses a prominent challenge considering that masses can be obscured in images and appear with irregular shapes and low image contrast. In this study, a multichannel, multiscale fully convolutional network is proposed and evaluated for mass segmentation in mammograms. To reduce the impact of surrounding unrelated structures, preprocessed images with a salient mass appearance are obtained as the second input channel of the network. Furthermore, to jointly conduct fine boundary delineation and global mass localization, we incorporate more crucial context information by learning multiscale features from different resolution levels. The performance of our segmentation approach is compared with that of several traditional and deep‐learning‐based methods on the popular DDSM and INbreast datasets. The evaluation indices consist of the Dice similarity coefficient, area overlap measure, area undersegmentation measure, area oversegmentation measure, and Hausdorff distance. The mean values of the Dice similarity coefficient and Hausdorff distance of our proposed segmentation method are 0.915 ± 0.031 and 6.257 ± 3.380, respectively, on DDSM and 0.918 ± 0.038 and 2.572 ± 0.956, respectively, on INbreast, which are superior to those of the existing methods. The experimental results verify that our proposed multichannel, multiscale fully convolutional network can reliably segment masses in mammograms.

中文翻译：

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使用多通道和多尺度全卷积网络进行乳腺 X 线图像质量分割

乳腺癌是全球女性死亡的主要原因之一。乳房 X 线图像质量分割是乳房 X 线图像分析的一项重要任务。然而，考虑到肿块可能会在图像中被遮挡并以不规则的形状和低图像对比度出现，这个过程提出了一个突出的挑战。在这项研究中，提出并评估了一种多通道、多尺度全卷积网络，用于乳房 X 光照片中的质量分割。为了减少周围无关结构的影响，获得具有显着质量外观的预处理图像作为网络的第二个输入通道。此外，为了共同进行精细边界描绘和全局质量定位，我们通过学习不同分辨率级别的多尺度特征来整合更重要的上下文信息。我们的分割方法的性能与流行的 DDSM 和 INbreast 数据集上的几种传统和基于深度学习的方法的性能进行了比较。评价指标包括Dice相似系数、区域重叠测度、区域欠分割测度、区域过分割测度和Hausdorff距离。我们提出的分割方法的 Dice 相似系数和 Hausdorff 距离的平均值在 DDSM 上分别为 0.915±0.031 和 6.257±3.380，在 INbreast 上分别为 0.918±0.038 和 2.572±0.956，优于 INbreast现有的方法。实验结果验证了我们提出的多通道、多尺度全卷积网络可以可靠地分割乳房 X 光照片中的质量。