Complex scenario of ultrasound image, in which adjacent tissues (i.e., background) share similar intensity with and even contain richer texture patterns than lesion region (i.e., foreground), brings a unique challenge for accurate lesion segmentation. This work presents a decomposition-coupling network, called DC-Net, to deal with this challenge in a (foreground-background) saliency map disentanglement-fusion manner. The DC-Net consists of decomposition and coupling subnets, and the former preliminarily disentangles original image into foreground and background saliency maps, followed by the latter for accurate segmentation under the assistance of saliency prior fusion. The coupling subnet involves three aspects of fusion strategies, including: 1) regional feature aggregation (via differentiable context pooling operator in the encoder) to adaptively preserve local contextual details with the larger receptive field during dimension reduction; 2) relation-aware representation fusion (via cross-correlation fusion module in the decoder) to efficiently fuse low-level visual characteristics and high-level semantic features during resolution restoration; 3) dependency-aware prior incorporation (via coupler) to reinforce foreground-salient representation with the complementary information derived from background representation. Furthermore, a harmonic loss function is introduced to encourage the network to focus more attention on low-confidence and hard samples. The proposed method is evaluated on two ultrasound lesion segmentation tasks, which demonstrates the remarkable performance improvement over existing state-of-the-art methods.

翻译：超声图像的复杂场景中，相邻组织（即背景）与病灶区域（即前景）具有相似的灰度强度，甚至包含更丰富的纹理模式，这为精确的病灶分割带来了独特挑战。本文提出一种名为DC-Net的分解-耦合网络，通过（前景-背景）显著性图解缠与融合方式应对该挑战。DC-Net由分解子网和耦合子网构成，前者将原始图像初步解缠为前景显著性图和背景显著性图，后者在显著性先验融合辅助下实现精确分割。耦合子网包含三种融合策略：1）区域特征聚合（通过编码器中的可微分上下文池化算子），在降维过程中自适应保留具有更大感受野的局部上下文细节；2）关系感知表示融合（通过解码器中的互相关融合模块），在分辨率恢复过程中高效融合底层视觉特征与高层语义特征；3）依赖感知先验融合（通过耦合器），利用背景表示的互补信息强化前景显著性表示。此外，引入谐波损失函数促使网络更加关注低置信度与困难样本。该方法在两个超声病灶分割任务上的评估结果表明，其相较于现有最先进方法实现了显著的性能提升。