Decomposing and Coupling Saliency Map for Lesion Segmentation in
  Ultrasound Images
Zhenyuan Ning Yixiao Mao Qianjin Feng Shengzhou Zhong Yu Zhang
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.