Automatic, Robust, and Globally Optimal Segmentation of Tubular Structures

We present an automatic three-dimensional segmentation approach based on continuous max flow that targets tubular structures in medical images. Our method uses second-order derivative information provided by Frangi et al.’s vesselness feature and exploits it twofold: First, the vesselness response itself is used for localizing the tubular structure of interest. Second, the eigenvectors of the Hessian eigendecomposition guide our anisotropic total variation–regularized segmentation. In a simulation experiment, we demonstrate the superiority of anisotropic as compared to isotropic total variation–regularized segmentation in the presence of noise. In an experiment with magnetic resonance images of the human cervical spinal cord, we compare our automated segmentations to those of two human observers. Finally, a comparison with a dedicated state-of-the-art spinal cord segmentation framework shows that we achieve comparable to superior segmentation quality.