Liver lesion segmentation in ultrasound: A benchmark and a baseline network.

Accurate liver lesion segmentation in ultrasound is a challenging task due to high speckle noise, ambiguous lesion boundaries, and inhomogeneous intensity distribution inside the lesion regions. This work first collected and annotated a dataset for liver lesion segmentation in ultrasound. In this paper, we propose a novel convolutional neural network to learn dual self-attentive transformer features for boosting liver lesion segmentation by leveraging the complementary information among non-local features encoded at different layers of the transformer architecture. To do so, we devise a dual self-attention refinement (DSR) module to synergistically utilize self-attention and reverse self-attention mechanisms to extract complementary lesion characteristics between cascaded multi-layer feature maps, assisting the model to produce more accurate segmentation results. Moreover, we propose a False-Positive-Negative loss to enable our network to further suppress the non-liver-lesion noise at shallow transformer layers and enhance more target liver lesion details into CNN features at deep transformer layers. Experimental results show that our network outperforms state-of-the-art methods quantitatively and qualitatively.