13 March 2013 Skeleton-based refinement of multi-material volumetric meshes
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Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 866936 (2013) https://doi.org/10.1117/12.2006769
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
Accurate multi-material mesh generation is necessary for many applications, e.g. image-guided surgery, in which precision is important. For this application, it is necessary to enhance conventional algorithms with physiological information that adds accuracy to the results. There are several approaches working on the generation of such meshes. However, state of the art approaches show inaccuracies in the areas in which thin structures are, e.g. liver vasculature. These algorithms are not able to detect the vessels in areas in which they are narrow and they assign their elements to wrong materials, e.g., parenchyma. We propose to extend two state of the art algorithms, namely that by Boltcheva et al. and that by Pons et al. and enhance them making use of the skeleton of these structures to solve this problem. By analyzing the mesh generated by the aforementioned algorithms one can find several intersections between the mesh belonging to the vessels and the skeleton, showing that some elements must be mismatched. We evaluate the proposed algorithm in 23 clinical datasets of the liver, in which we previously segmented parenchyma and vessels. For quantitative evaluation, the meshes generated with and without skeleton information are compared. The improvements are shown by means of intersection number, volume and length differences of the vasculature mesh using the different methods. The results show an improvement of 65% for the number of intersections, 4% for the volume and 22% for the length.
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Cristina Oyarzun Laura, Cristina Oyarzun Laura, Pablo Bueno Plaza, Pablo Bueno Plaza, Klaus Drechsler, Klaus Drechsler, Stefan Wesarg, Stefan Wesarg, } "Skeleton-based refinement of multi-material volumetric meshes", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866936 (13 March 2013); doi: 10.1117/12.2006769; https://doi.org/10.1117/12.2006769
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