Liver and Lesion Segmentation Algorithm for Contrast Enhanced CT Images

Automatic liver segmentation is a crucial step for aiding in liver surgery and in diagnosing liver pathologies. Its goal is to identify the important anatomical structures as the liver segments, the vessel tree, present lesion and tumors. Nowadays, the current clinical practice is to use volumetric dynamic contrast enhanced computer tomography images, acquired before and after contrast agent injection. However, currently, liver computer aided diagnosis systems work with a single volumetric image, i.e. the volume exhibiting the best contrast enhancement. Therefore, the motivation of our work is to explore the gray-level enhancement in the different abdominal tissues and organs present in all acquired volumes (phases). The described method first brings into alignment all volumes. The segmentation combines an initial clustering approach with the EM algorithm to optimally fit a sum of multivariate Gaussian distributions to the multidimensional joint histogram. The segmentation is performed in an hierarchical way: the whole abdominal volume is segmented in the first step, the segmentation is applied only on the detected liver region in the second step and only at the individual lesions in the third step. We give a comparison of the segmentation results to the ground truth data obtained via manual segmentation by a radiologist. Experiments were performed on twenty-four dual-phase studies. The results show substantial improvement in the liver segmentation and a minor improvement in the lesion segmentation results, compared to single-phase segmentation.