A work-efficient GPU algorithm for level set segmentation

Mike Roberts, Mario Costa Sousa, Joseph Ross Mitchell
University of Calgary, Canada


   title={A work-efficient GPU algorithm for level set segmentation},

   author={Roberts, M. and Sousa, M.C. and Mitchell, J.R.},

   booktitle={ACM SIGGRAPH 2010 Posters},





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We present a novel GPU level set segmentation algorithm that is both work-efficient and step-efficient. Our algorithm has O(log n) step-complexity, in contrast to previous GPU algorithms [Lefohn et al. 2004; Jeong et al. 2009] which have O(n) step-complexity. Moreover our algorithm limits the active computational domain to the minimal set of changing elements by examining both the temporal and spatial derivatives of the level set field. We apply our algorithm to 3D medical images (Figure 1) and demonstrate that our algorithm reduces the total number of processed level set field elements by 16x and is 14x faster than previous GPU algorithms with no reduction in segmentation accuracy.
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