Automatic liver segmentation from CT volumes based on multi-view information fusion and condition random fields

Liver segmentation from CT images is a fundamental and essential step for many clinical applications, such as disease diagnosis, surgery navigator, and radiation therapy. Due to the limited computing resources, automatic and accurate liver segmentation from 3D CT volume is still a challenge task. In this paper, we propose an automatic liver segmentation method by fusing multi-view information. First, a 2D network combined with Dual Self-Attention (DSA) is proposed and applied on multi-sectional 2D slices reconstructed from axial, coronal, and sagittal directions. Then, to generate 3D segmentation results, a lightweight 3D network is designed to fuse the segmentation results from different viewing. Finally, a full connection condition random field is used to refine the 3D results. The proposed method is evaluated on four public datasets, including 3DIRCADb, CHAOS, Sliver07, and LiTS. The experimental results show that the proposed method can accurately segment the liver region for the datasets with large or small cases, as well as healthy or lesion livers. The dice values obtained by the proposed method on four datasets are 0.952, 0.969, 0.958, and 0.964, respectively, which are better than those obtained by many existing ones.