Scalable and Interactive Segmentation and Visualization of Neural Processes in EM Datasets
IEEE Transactions on Visualization and Computer Graphics
A streaming narrow-band algorithm: interactive computation and visualization of level sets
IEEE Transactions on Visualization and Computer Graphics
Optimal multi-image processing streaming framework on parallel heterogeneous systems
EG PGV'11 Proceedings of the 11th Eurographics conference on Parallel Graphics and Visualization
Efficient application of GPGPU for lava flow hazard mapping
The Journal of Supercomputing
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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.