A variational approach to liver segmentation using statistics from multiple sources.

Medical image segmentation plays an important role in digital medical research, and therapy planning and delivery. However, the presence of noise and low contrast renders automatic liver segmentation an extremely challenging task. In this study, we focus on a variational approach to liver segmentation in computed tomography scan volumes in a semiautomatic and slice-by-slice manner. In this method, one slice is selected and its connected component liver region is determined manually to initialize the subsequent automatic segmentation process. From this guiding slice, we execute the proposed method downward to the last one and upward to the first one, respectively. A segmentation energy function is proposed by combining the statistical shape prior, global Gaussian intensity analysis, and enforced local statistical feature under the level set framework. During segmentation, the shape of the liver shape is estimated by minimization of this function. The improved Chan-Vese model is used to refine the shape to capture the long and narrow regions of the liver. The proposed method was verified on two independent public databases, the 3D-IRCADb and the SLIVER07. Among all the tested methods, our method yielded the best volumetric overlap error (VOE) of [Formula: see text], the best root mean square symmetric surface distance (RMSD) of [Formula: see text] mm, the best maximum symmetric surface distance (MSD) of [Formula: see text] mm in 3D-IRCADb dataset, and the best average symmetric surface distance (ASD) of [Formula: see text] mm, the best RMSD of [Formula: see text] mm in SLIVER07 dataset, respectively. The results of the quantitative comparison show that the proposed liver segmentation method achieves competitive segmentation performance with state-of-the-art techniques.