Hepatectomies are resections in which segments of the liver are extracted. While medical images are fundamental in the surgery planning procedure, the process of analysis of such images slice-by-slice is still tedious and inefficient. In this work we propose a strategy to efficiently and semi-automatically segment and classify patient-specific liver models in 3D through a mobile display device. The method is based on volume visualization of standard CT datasets and allows accurate estimation of functional remaining liver volume. Experiments showing effectiveness of the method are presented, and quantitative and qualitative results are discussed.