Segmentation of interwoven 3d tubular tree structures utilizing shape priors and graph cuts

The segmentation of tubular tree structures like vessel systems in volumetric datasets is of vital interest for many medical applications. We present a novel approach that allows to simultaneously separate and segment multiple interwoven tubular tree structures. The algorithm consists of two main processing steps. First, the tree structures are identified and corresponding shape priors are generated by using a bottom-up identification of tubular objects combined with a top-down grouping of these objects into complete tree structures. The grouping step allows us to separate interwoven trees and to handle local disturbances. Second, the generated shape priors are utilized for the intrinsic segmentation of the different tubular systems to avoid leakage or undersegmentation in locally disturbed regions. We have evaluated our method on phantom and different clinical CT datasets and demonstrated its ability to correctly obtain/separate different tree structures, accurately determine the surface of tubular tree structures, and robustly handle noise, disturbances (e.g., tumors), and deviations from cylindrical tube shapes like for example aneurysms.