Automatic segmentation of the liver from multi- and single-phase contrast-enhanced CT images

Segmentation of contrast-enhanced abdominal CT images is required by many clinical applications of computer aided diagnosis and therapy planning. The research on automated methods involves different organs among which the liver is the most emphasized. In the current clinical practice more images (at different phases) are acquired from the region of interest in case of a contrast-enhanced abdominal CT examination. The majority of the existing methods, however, use only the portal-venous image to segment the liver. This paper presents a method that automatically segments the liver by combining more phases of the contrast-enhanced CT examination. The method uses region-growing facilitated by pre- and post-processing functions, which incorporate anatomical and multi-phase information to eliminate over- and under-segmentation. Another method, which uses only the portal-venous phase to segment the liver automatically, is also presented. Both methods were evaluated using different datasets, which showed that the result of multi-phase method can be used without or after minor correction in nearly 94% of the cases, and the single-phase method can provide result comparable with non-expert manual segmentation in 90% of the cases. The comparison of the two methods demonstrates that automatic segmentation is more reliable when the information of more phases is combined.