Semi-automatic anatomical tree matching for landmark-based elastic registration of liver volumes

One promising approach to register liver volume acquisitions is based on the branching points of the vessel trees as anatomical landmarks inherently available in the liver. Automated tree matching algorithms were proposed to automatically find pair-wise correspondences between two vessel trees. However, to the best of our knowledge, none of the existing automatic methods are completely error free. After a review of current literature and methodologies on the topic, we propose an efficient interaction method that can be employed to support tree matching algorithms with important pre-selected correspondences or after an automatic matching to manually correct wrongly matched nodes. We used this method in combination with a promising automatic tree matching algorithm also presented in this work. The proposed method was evaluated by 4 participants and a CT dataset that we used to derive multiple artificial datasets.