A statistical assembled deformable model (SAMTUS) for vasculature reconstruction

Although many deformable models have been proposed in medical applications for segmenting isolated structures in the human anatomy, not much of such work had been done on tubular structures such as the vasculature. In this paper, we propose a statistical assembled model for tubular structures (SAMTUS) to segment entire tubular structure from three-dimensional (3D) volumetric data. To our knowledge, there is no literature about the statistical deformable model for entire tubular structures. Specifically, the statistical tubular model is composed of a statistical axis model (SAM) and a statistical surface model (SSM). Both of them are assembled from a set of branch segments through the control points. Instead of searching for fuzzy correspondence along tubular axes or surfaces, we build point matching between feature points along tubular segments, and train SAM and SSM independently to characterize, respectively, the axial and the cross-sectional variation of the entire structure. In this way, more accurate point correspondence can be established, and a larger number of deformation modes can be captured. Our SAMTUS-based segmentation process consists of three stages: initialization, model fitting and final refinement. The experimental results demonstrate that the algorithm obtains good quantifications on the morphology and volume of the vasculature of the zebrafish which is being used increasingly as a specimen for drug screening and genomic research.