Implementation and use of 3D pairwise geodesic distance fields for seeding abdominal aortic vessels

PurposePrecise extraction of aorta and the vessels departing from it (i.e. coeliac, renal, and iliac) is vital for correct positioning of a graft prior to abdominal aortic surgery. To perform this task, most of the segmentation algorithms rely on seed points, and better-located seed points provide better initial positions for cross-sectional methods. Under non-optimal acquisition characteristics of daily clinical routine and complex morphology of these vessels, inserting seed points to all these small, but critically important vessels is a tedious, time-consuming, and error-prone task. Thus, in this paper, a novel strategy is developed to generate pathways between user-inserted seed points in order to initialize segmentation methods effectively.MethodThe proposed method requires only a single user-inserted seed for each vessel of interest for initializations. Starting from these initial seeds, it automatically generates pathways that span all vessels in between. To accomplish this, first, a geodesic mask is generated by adaptive thresholding, which reinforces the initial seeds to be kept in the vascular tree. Then, a novel implementation of 3D pairwise geodesic distance field (3D-PGDF) is utilized. It is shown that the minimal-valued geodesic of 3D-PGDF successfully defines a path linking the initial seeds as being the shortest geodesic. Moreover, the robustness of the minimum level set of the 3D-PGDF to local variations and regions of high curvature is increased by a region classification strategy, which adds partial geodesics to these critical regions.ResultsThe proposed method was applied to 19 challenging CT data sets obtained from four different scanners and compared to two benchmark methods. The first method is a high-precision technique with very long processing time (subvoxel precise multi-stencil fast marching—MSFM), while the second is a very fast method with lower accuracy (3D fast marching). The results, which are obtained using various measures, show that the pathways generated by the developed technique enable significantly higher segmentation performance than 3D fast marching and require much less computational power and time than MSFM.ConclusionThe developed technique offers a useful tool for generating pathways between seed points with minimal user interaction. It guarantees to include all important vessels in a computationally effective manner and thus, it can be used to initialize segmentation methods for abdominal aortic tree.