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Issue No. 06 - November/December (2009 vol. 15)
ISSN: 1077-2626
pp: 1505-1514
Hanspeter Pfister , School of Engineering and Applied Sciences at Harvard University
Amelio Vazquez , School of Engineering and Applied Sciences at Harvard University
Won-Ki Jeong , School of Engineering and Applied Sciences at Harvard University
Markus Hadwiger , VRVis Center for Virtual Reality and Visualization Research
Ross T. Whitaker , Scientific Computing and Imaging Institute at the University of Utah
Johanna Beyer , VRVis Center for Virtual Reality and Visualization Research
ABSTRACT
Recent advances in scanning technology provide high resolution EM (Electron Microscopy) datasets that allow neuro-scientists to reconstruct complex neural connections in a nervous system. However, due to the enormous size and complexity of the resulting data, segmentation and visualization of neural processes in EM data is usually a difficult and very time-consuming task. In this paper, we present NeuroTrace, a novel EM volume segmentation and visualization system that consists of two parts: a semi-automatic multiphase level set segmentation with 3D tracking for reconstruction of neural processes, and a specialized volume rendering approach for visualization of EM volumes. It employs view-dependent on-demand filtering and evaluation of a local histogram edge metric, as well as on-the-fly interpolation and ray-casting of implicit surfaces for segmented neural structures. Both methods are implemented on the GPU for interactive performance. NeuroTrace is designed to be scalable to large datasets and data-parallel hardware architectures. A comparison of NeuroTrace with a commonly used manual EM segmentation tool shows that our interactive workflow is faster and easier to use for the reconstruction of complex neural processes.
INDEX TERMS
Segmentation, neuroscience, connectome, volume rendering, implicit surface rendering, graphics hardware
CITATION
Hanspeter Pfister, Amelio Vazquez, Won-Ki Jeong, Markus Hadwiger, Ross T. Whitaker, Johanna Beyer, "Scalable and Interactive Segmentation and Visualization of Neural Processes in EM Datasets", IEEE Transactions on Visualization & Computer Graphics, vol. 15, no. , pp. 1505-1514, November/December 2009, doi:10.1109/TVCG.2009.178
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