Asymmetric U-shaped network with hybrid attention mechanism for kidney ultrasound images segmentation

Kidney ultrasound (KUS) images segmentation is one of the key steps in computer-aided diagnosis. The perturbation of heterogeneous structure, similar intensity distribution, and kidney morphology pose challenges for the segmentation of KUS images. In this paper, we proposed an asymmetric U-shaped network based on the U -net core architecture to segment KUS images accurately and reliably. Specifically, the architecture mainly consists of a dense residual connection encoder, a multi-step up-sampling decoder with the hybrid attention module, and a side-out deep supervision module. The design of the dense residual connection encoder can capture sufficient kidney feature information to improve the representation ability of the network. The devel-opment of the hybrid attention module can further guide the network to pay more attention to the representation of the kidney. In addition, the introduction of the side-out deep supervision model can help the network obtain segmentation results that are closer to the ground-truth masks. Moreover, to reduce network parameters, we proposed a multi-step up-sampling optimization strategy to simplify the design of the network. We compare with several state-of-the-art medical image segmentation methods on the same KUS dataset using seven quantitative metrics. The results of our method on Jaccard, Dice, Accuracy, Recall, Precision, ASSD and AUC are 89.95%, 94.59%, 98.65%, 94.47%, 95.07%, 0.3006 and 0.9703, respectively. Experimental results demonstrate that the proposed method achieves the most competitive segmentation performance on KUS images.