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A GPU framework for parallel segmentation of volumetric images using discrete deformable models

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Abstract

Despite the ability of current GPU processors to treat heavy parallel computation tasks, its use for solving medical image segmentation problems is still not fully exploited and remains challenging. A lot of difficulties may arise related to, for example, the different image modalities, noise and artifacts of source images, or the shape and appearance variability of the structures to segment. Motivated by practical problems of image segmentation in the medical field, we present in this paper a GPU framework based on explicit discrete deformable models, implemented over the NVidia CUDA architecture, aimed for the segmentation of volumetric images. The framework supports the segmentation in parallel of different volumetric structures as well as interaction during the segmentation process and real-time visualization of the intermediate results. Promising results in terms of accuracy and speed on a real segmentation experiment have demonstrated the usability of the system.

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Authors and Affiliations

  1. MIRALab, University of Geneva, Battelle, 1227, Carouge, Switzerland

    Jérôme Schmid & Nadia Magnenat-Thalmann

  2. CRS4 Visual Computing Group, Polaris ed. 1, 09010, Pula, Italy

    José A. Iglesias Guitián & Enrico Gobbetti

  3. Institute for Media Innovation, Nanyang Technological University, Singapore, Singapore

    Nadia Magnenat-Thalmann

Authors
  1. Jérôme Schmid
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  2. José A. Iglesias Guitián
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  3. Enrico Gobbetti
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  4. Nadia Magnenat-Thalmann
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Correspondence to Jérôme Schmid.

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Schmid, J., Iglesias Guitián, J.A., Gobbetti, E. et al. A GPU framework for parallel segmentation of volumetric images using discrete deformable models. Vis Comput 27, 85–95 (2011). https://doi.org/10.1007/s00371-010-0532-0

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