A GPU Based Diffusion Method for Whole-Heart and Great Vessel Segmentation
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Segmenting the blood pool and myocardium from a 3D cardiovascular magnetic resonance (CMR) image allows to create a patient-specific heart model for surgical planning in children with complex congenital heart disease (CHD). Implementation of semi-automatic or automatic segmentation algorithms is challenging because of a high anatomical variability of the heart defects, low contrast, and intensity variations in the images. Therefore, manual segmentation is the gold standard but it is labor-intensive. In this paper we report the set-up and results of a highly scalable semi-automatic diffusion algorithm for image segmentation. The method extrapolates the information from a small number of expert manually labeled reference slices to the remaining volume. While results of most semi-automatic algorithms strongly depend on well-chosen but usually unknown parameters this approach is parameter-free. Validation is performed on twenty 3D CMR images.
KeywordsSegmentation Diffusion Random walks Interactive segmentation Semi-automatic segmentation Multi-GPU
This work was carried out with the support of the Federal Ministry of Education and Research (BMBF), Germany, within the collaboration center ASTOR (Arthropod Structure revealed by ultra-fast Tomography and Online Reconstruction) and NOVA (Network for Online Visualization and synergistic Analysis of Tomographic Data).
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