IEEE Trans Med Imaging. 2023 May;42(5):1289-1300. doi: 10.1109/TMI.2022.3226268. Epub 2023 May 2.
Various deep learning methods have been proposed to segment breast lesions from ultrasound images. However, similar intensity distributions, variable tumor morphologies and blurred boundaries present challenges for breast lesions segmentation, especially for malignant tumors with irregular shapes. Considering the complexity of ultrasound images, we develop an adaptive attention U-net (AAU-net) to segment breast lesions automatically and stably from ultrasound images. Specifically, we introduce a hybrid adaptive attention module (HAAM), which mainly consists of a channel self-attention block and a spatial self-attention block, to replace the traditional convolution operation. Compared with the conventional convolution operation, the design of the hybrid adaptive attention module can help us capture more features under different receptive fields. Different from existing attention mechanisms, the HAAM module can guide the network to adaptively select more robust representation in channel and space dimensions to cope with more complex breast lesions segmentation. Extensive experiments with several state-of-the-art deep learning segmentation methods on three public breast ultrasound datasets show that our method has better performance on breast lesions segmentation. Furthermore, robustness analysis and external experiments demonstrate that our proposed AAU-net has better generalization performance in the breast lesion segmentation. Moreover, the HAAM module can be flexibly applied to existing network frameworks. The source code is available on https://github.com/CGPxy/AAU-net.
已经提出了各种深度学习方法来对超声图像中的乳腺病变进行分割。然而，相似的强度分布、肿瘤形态的多变性和边界的模糊性给乳腺病变的分割带来了挑战，特别是对于形状不规则的恶性肿瘤。考虑到超声图像的复杂性，我们开发了一种自适应注意 U-net (AAU-net)，以自动、稳定地从超声图像中分割乳腺病变。具体来说，我们引入了一种混合自适应注意模块 (HAAM)，它主要由一个通道自注意块和一个空间自注意块组成，以取代传统的卷积操作。与传统的卷积操作相比，混合自适应注意模块的设计可以帮助我们在不同的感受野下捕捉更多的特征。与现有的注意力机制不同，HAAM 模块可以引导网络自适应地在通道和空间维度上选择更稳健的表示，以应对更复杂的乳腺病变分割。在三个公共乳腺超声数据集上，我们使用几种最先进的深度学习分割方法进行了广泛的实验，结果表明，我们的方法在乳腺病变分割方面具有更好的性能。此外，鲁棒性分析和外部实验表明，我们提出的 AAU-net 在乳腺病变分割中具有更好的泛化性能。此外，HAAM 模块可以灵活地应用于现有的网络框架。源代码可在 https://github.com/CGPxy/AAU-net 上获得。
