부산대학교 도서관

In this paper, an automatic coronary tree labeling algorithm is developed for labeling the extracted branches with their anatomical names for CCTA datasets. A two-step matching algorithm is implemented by means of a statistical coronary tree model. The main branches are first identified in a registration step. Then all the segments including proximal, middle and distal parts of the main branches and all side-branches in the coronary tree are labeled. Additional clinical criteria are used to generate the final result. Fifty-eight CCTA datasets with right-dominant coronary trees were used for the evaluation. Compared with manually corrected results by an expert, 37 labels (4.76%) in the automatic results were needed to be changed or removed. For the remaining 741 labels obtained by the automatic method, the average overlap measurement between the expert reference and automatic results was 91.41%.