Abstract
In medical imaging, the generation of surface representations of anatomical objects obtained by labeling images from various modalities, is a critical component for visualization, simulation, and analysis. The interfaces between labeled regions can meet at arbitrary angles and with complex topologies, causing most automatic meshing algorithms to fail. We apply a recent Delaunay refinement algorithm to generate high quality triangular meshes that approximate the interface surfaces. This algorithm has proven guarantees for meshing piecewise-smooth shapes and its implementation overhead is low. Consequently, the approach is applicable to labeled datasets generated from binary segmentations as well as from probabilistic segmentation algorithms. We show the effectiveness of this technique on data from a variety of medical fields and discuss its ability to control the quality and size of the output meshes. The same algorithm can be used to generate tetrahedral meshes of the segmentation space.









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Acknowledgments
Datasets were obtained with the aid of Thomas Kerwin, Miriah Meyer, and Ross Whitaker. The temporal bone and dog skull are provided by Don Stredney at the Ohio Supercomputer Center. The tomato and orange were produced at Lawrence Berkeley Laboratory by Bill Johnston and Wing Nip of the Information and Computing Sciences Division. The torso is courtesy of John Triedman and Matthew Jolley at Boston Children’s Hospital.
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This work was supported by NSF grants CCF-0430735 and CCF-0635008.
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Dey, T.K., Janoos, F. & Levine, J.A. Meshing interfaces of multi-label data with Delaunay refinement. Engineering with Computers 28, 71–82 (2012). https://doi.org/10.1007/s00366-011-0217-y
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DOI: https://doi.org/10.1007/s00366-011-0217-y