Lee Haeyun, Park Jinhyoung, Hwang Jae Youn
IEEE Trans Ultrason Ferroelectr Freq Control. 2020 Jul;67(7):1344-1353. doi: 10.1109/TUFFC.2020.2972573. Epub 2020 Feb 10.
Breast cancer accounts for the second-largest number of deaths in women around the world, and more than 8% of women will suffer from the disease in their lifetime. Mortality due to breast cancer can be reduced by its early and precise diagnosis. Many studies have investigated methods for segmentation, and computer-aided diagnosis based on deep learning techniques, in particular, has recently gained attention. However, recently proposed methods such as fully convolutional network (FCN), SegNet, and U-Net still need to be further improved to provide better semantic segmentation when diagnosing breast cancer by ultrasound imaging, because of their low performance. In this article, we propose a channel attention module with multiscale grid average pooling (MSGRAP) for the precise segmentation of breast cancer regions in ultrasound images. We demonstrate the effectiveness of the channel attention module with MSGRAP for semantic segmentation and develop a novel semantic segmentation network with the proposed attention module for the precise segmentation of breast cancer regions in ultrasound images. While a conventional convolutional operation cannot use global spatial information on input images and only use the small local information in a kernel of a convolution filter, the proposed attention module allows using both global and local spatial information. In addition, through ablation studies, we come up with a network architecture for precise breast cancer segmentation in an ultrasound image. The proposed network was constructed with an open-source breast cancer ultrasound image data set, and its performance was compared with those of other state-of-the-art deep-learning models for the segmentation of breast cancer. The experimental results showed that our network outperformed other segmentation methods, and the proposed channel attention module improved the performance of the network for breast cancer segmentation in ultrasound images.
乳腺癌是全球女性中导致死亡人数第二多的疾病，超过8%的女性在其一生中会患上这种疾病。早期准确诊断乳腺癌可降低其死亡率。许多研究探讨了分割方法，特别是基于深度学习技术的计算机辅助诊断最近受到了关注。然而，最近提出的方法，如全卷积网络（FCN）、SegNet和U-Net，在通过超声成像诊断乳腺癌时，由于性能较低，仍需进一步改进以提供更好的语义分割。在本文中，我们提出了一种具有多尺度网格平均池化（MSGRAP）的通道注意力模块，用于超声图像中乳腺癌区域的精确分割。我们展示了带有MSGRAP的通道注意力模块在语义分割方面的有效性，并开发了一种新颖的语义分割网络，该网络带有所提出的注意力模块，用于超声图像中乳腺癌区域的精确分割。传统的卷积操作无法利用输入图像的全局空间信息，只能使用卷积滤波器内核中的小局部信息，而所提出的注意力模块允许同时使用全局和局部空间信息。此外，通过消融研究，我们提出了一种用于超声图像中乳腺癌精确分割的网络架构。所提出的网络是使用开源乳腺癌超声图像数据集构建的，并将其性能与其他用于乳腺癌分割的先进深度学习模型的性能进行了比较。实验结果表明，我们的网络优于其他分割方法，并且所提出的通道注意力模块提高了网络在超声图像中乳腺癌分割的性能。
