Deep learning methods, especially convolutional neural networks, have been successfully applied to lesion segmentation in breast ultrasound (BUS) images. However, pattern complexity and intensity similarity between the surrounding tissues (i.e., background) and lesion regions (i.e., foreground) bring challenges for lesion segmentation. Considering that such rich texture information is contained in background, very few methods have tried to explore and exploit background-salient representations for assisting foreground segmentation. Additionally, other characteristics of BUS images, i.e., 1) low-contrast appearance and blurry boundary, and 2) significant shape and position variation of lesions, also increase the difficulty in accurate lesion segmentation. In this paper, we present a saliency-guided morphology-aware U-Net (SMU-Net) for lesion segmentation in BUS images. The SMU-Net is composed of a main network with an additional middle stream and an auxiliary network. Specifically, we first propose generation of saliency maps which incorporate both low-level and high-level image structures, for foreground and background. These saliency maps are then employed to guide the main network and auxiliary network for respectively learning foreground-salient and background-salient representations. Furthermore, we devise an additional middle stream which basically consists of background-assisted fusion, shape-aware, edge-aware and position-aware units. This stream receives the coarse-to-fine representations from the main network and auxiliary network for efficiently fusing the foreground-salient and background-salient features and enhancing the ability of learning morphological information for network. Extensive experiments on five datasets demonstrate higher performance and superior robustness to the scale of dataset than several state-of-the-art deep learning approaches in breast lesion segmentation in ultrasound image.

中文翻译：

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SMU-Net：用于超声图像中乳腺病变分割的显着性引导形态学感知 U-Net

深度学习方法，尤其是卷积神经网络，已成功应用于乳腺超声 (BUS) 图像中的病变分割。然而，周围组织（即背景）和病变区域（即前景）之间的模式复杂性和强度相似性给病变分割带来了挑战。考虑到背景中包含如此丰富的纹理信息，很少有方法尝试探索和利用背景显着表示来辅助前景分割。此外，BUS图像的其他特点，即1）低对比度的外观和模糊的边界，以及2）明显的病灶形状和位置变化，也增加了准确分割病灶的难度。在本文中，我们提出了一种显着性引导的形态感知 U-Net (SMU-Net)，用于 BUS 图像中的病变分割。SMU-Net 由一个主网络和一个附加的中间流和一个辅助网络组成。具体来说，我们首先建议生成包含低级和高级图像结构的显着性图，用于前景和背景。然后使用这些显着图来指导主网络和辅助网络分别学习前景显着和背景显着表示。此外，我们设计了一个额外的中间流，它基本上由背景辅助融合、形状感知、边缘感知和位置感知单元组成。该流接收来自主网络和辅助网络的从粗到细的表示，用于有效融合前景显着和背景显着特征，增强网络学习形态信息的能力。在五个数据集上进行的广泛实验表明，在超声图像中的乳房病变分割中，与几种最先进的深度学习方法相比，它具有更高的性能和对数据集规模的鲁棒性。