Liver segmentation using superpixel-based graph cuts and restricted regions of shape constrains

Abstract

Liver segmentation is one of the most fundamental and challenging tasks in computer aided diagnosis (CAD) system for liver diseases. Graph cut algorithms have been successfully applied to medical image segmentation of different organs for 3D volume data, which not only leads to very large-scale graph due to the same node number as voxel number, but also completely ignore some available organ shape priors. Thus, a slice by slice liver segmentation method by combining shape constraints according to previously slice segmentation has been proposed based on graph cut. However, the constructed graph scale is still large, and the computation of distance map from all voxel to the segmented shape leads to high cost. In order to explore an efficient and effective slice by slice segmentation method for liver, this paper proposes to apply clustering algorithm to firstly group slice pixels into superpixels as nodes for constructing graph, which not only greatly reduce the graph scale but also significantly speed up the optimization procedure of the graph. Furthermore, we restrict the regions near organ boundary as shape constraints, which can further reduce computational time. To validate effectiveness and efficiency of our proposed method, we conduct experiments on 10 CT volumes, most of which have tumors inside liver, and abnormal deformed shape of liver. Our method can yield an average dice coefficient: 0.94, about 659.22 second in computation, and take only 1.5GB in memory usage.