AHCNet: An Application of Attention Mechanism and Hybrid Connection for Liver Tumor Segmentation in CT Volumes

Abstract

The liver is a common site for the development of primary (i.e., originating from the liver, e.g., hepatocellular carcinoma) or secondary (i.e., spread to the liver, e.g., colorectal cancer) tumor. Due to its complex background, heterogeneous, and diffusive shape, automatic segmentation of tumor remains a challenging task. So far, only the interactive method has been adopted to obtain the acceptable segmentation results of a liver tumor. In this paper, we design an Attention Hybrid Connection Network architecture which combines soft and hard attention mechanism and long and short skip connections. We also propose a cascade network based on the liver localization network, liver segmentation network, and tumor segmentation network to cope with this challenge. Simultaneously, the joint dice loss function is proposed to train the liver localization network to obtain the accurate 3D liver bounding box, and the focal binary cross entropy is used as a loss function to fine-tune the tumor segmentation network for detecting more potentially malignant tumor and reduce false positives. Our framework is trained using the 110 cases in the LiTS dataset and extensively evaluated by the 20 cases in the 3DIRCADb dataset and the 117 cases in the Clinical dataset, which indicates that the proposed method can achieve faster network convergence and accurate semantic segmentation and further demonstrate that the proposed method has a good clinical value.