SEU2-Net: multi-scale U2-Net with SE attention mechanism for liver occupying lesion CT image segmentation

Abstract

Liver occupying lesions can profoundly impact an individual's health and well-being. To assist physicians in the diagnosis and treatment of abnormal areas in the liver, we propose a novel network named SEU²-Net by introducing the channel attention mechanism into U²-Net for accurate and automatic liver occupying lesion segmentation. We design the Residual U-block with Squeeze-and-Excitation (SE-RSU), which is to add the Squeeze-and-Excitation (SE) attention mechanism at the residual connections of the Residual U-blocks (RSU, the component unit of U²-Net). SEU²-Net not only retains the advantages of U²-Net in capturing contextual information at multiple scales, but can also adaptively recalibrate channel feature responses to emphasize useful feature information according to the channel attention mechanism. In addition, we present a new abdominal CT dataset for liver occupying lesion segmentation from Peking University First Hospital's clinical data (PUFH dataset). We evaluate the proposed method and compare it with eight deep learning networks on the PUFH and the Liver Tumor Segmentation Challenge (LiTS) datasets. The experimental results show that SEU²-Net has state-of-the-art performance and good robustness in liver occupying lesions segmentation.

Keywords: CT; Deep learning; Liver occupying lesion segmentation; SE attention; U2-Net.