Abstract
In this work we propose a fast and flexible GPU 3D level-set segmentation framework able to handle different segmentation tasks. Experiments on simulated and real images demonstrate the method ability at achieving high computational efficiency with no reduction in segmentation accuracy compared to its sequential counterpart. The method clinical applicability is demonstrated by addressing the task of Left-Ventricle myocardium segmentation in Real-Time 3D Echocardiography.
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Acknowledgments
This research was partially funded by the Italian Ministry of Education, University and Research (PRIN 2010–2011).
The authors would like to thank Jan D’hooge and Daniel Barbosa (KU Leuven) for providing RT3DE data, and Olivier Bernard (Creatis-INSA, Lyon) for his helpful comments and suggestions.
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Galluzzo, F., De Marchi, L., Testoni, N., Masetti, G. (2016). A GPU 3D Segmentation Framework for Medical Imaging. In: De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. Lecture Notes in Electrical Engineering, vol 351. Springer, Cham. https://doi.org/10.1007/978-3-319-20227-3_14
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DOI: https://doi.org/10.1007/978-3-319-20227-3_14
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