The Community for Technology Leaders
RSS Icon
Issue No.06 - November/December (2009 vol.15)
pp: 1505-1514
Won-Ki Jeong , School of Engineering and Applied Sciences at Harvard University
Johanna Beyer , VRVis Center for Virtual Reality and Visualization Research
Markus Hadwiger , VRVis Center for Virtual Reality and Visualization Research
Amelio Vazquez , School of Engineering and Applied Sciences at Harvard University
Hanspeter Pfister , School of Engineering and Applied Sciences at Harvard University
Ross T. Whitaker , Scientific Computing and Imaging Institute at the University of Utah
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.
Segmentation, neuroscience, connectome, volume rendering, implicit surface rendering, graphics hardware
Won-Ki Jeong, Johanna Beyer, Markus Hadwiger, Amelio Vazquez, Hanspeter Pfister, Ross T. Whitaker, "Scalable and Interactive Segmentation and Visualization of Neural Processes in EM Datasets", IEEE Transactions on Visualization & Computer Graphics, vol.15, no. 6, pp. 1505-1514, November/December 2009, doi:10.1109/TVCG.2009.178
[1] P. Anandan A computational framework and an algorithm for the measurement of visual motion. Journal on Computer Vision, 2: 283–310, 1989.
[2] A. Bartesaghi, G. Sapiro, and S. Subramaniam An energy-based three-dimensional segmentation approach for the quantitative interpretation of electron tomograms. IEEE Trans. Image Proc, 14 (9): 1314–1323, September 2005.
[3] D. Bartz, and W. Straßer, Interactive exploration of extra- and intracranial blood vessels. In In Proc. of IEEE Visualization, pages 389–392, 1999.
[4] J. Beyer, M. Hadwiger, T. Möller, and L. Fritz Smooth mixed-resolution GPU volume rendering. In, IEEE International Symposium on Volume and Point-Based Graphics (VG '08), pages 163–170, 2008.
[5] J. Caban and P. Rheingans Texture-based transfer functions for direct volume rendering. , IEEE Transactions on Visualization and Computer Graphics (Proc. of IEEE Visualization '08), 14 (6): 1364–1371, 2008.
[6] U. Clarenz, M. Droske, and M. Rumpf Towards fast non–rigid registration. In, Inverse Problems, Image Analysis and Medical Imaging, AMS Special Session Interaction of Inverse Problems and Image Analysis, volume 313, pages 67–84. AMS, 2002.
[7] T. Deschamps and L. D. Cohen, Fast extraction of minimal paths in 3d images and applications to virtual endoscopy. Medical Image Analysis, 5: 281–299, 2001.
[8] L. R. Dice Measures of the amount of ecologic association between species. Ecology, 26: 297–302, 1945.
[9] J. C. Fiala Reconstruct: a free editor for serial section microscopy. Journal of Microscopy, 218 (1): 52–61, April 2005.
[10] M. Hadwiger, C. Sigg, H. Scharsach, K. Bühler, and M. Gross, Real-time ray-casting and advanced shading of discrete isosurfaces. Computer Graphics Forum (Proc. Eurographics 2005), 24 (3): 303–312, 2005.
[11] L. Hong, S. Muraki, A. Kaufman, D. Bartz, and T. He Virtual voyage: interactive navigation in the human colon. In SIGGRAPH 97 Conference Proceedings, pages 27–34, 1997.
[12] W.-K. Jeong, and R. T. Whitaker A fast iterative method for Eikonal equations. SIAM Journal on Scientific Computing, 30 (5): 2512–2534, 2008.
[13] E. Jurrus, M. Hardy, T. Tasdizen, P. Fletcher, P. Koshevoy, C.-B. Chien, W. Denk, and R. Whitaker Axon tracking in serial block-face scanning electron microscopy. Medical Image Analysis (MEDIA), 13 (1): 180–188, February 2009.
[14] G. Kindlmann, and J. Durkin Semi-automatic Generation of Transfer Functions for Direct Volume Rendering. In Proceedings of IEEE Volume Visualization '98, pages 79–86, 1998.
[15] C. Kirbas, and F. Quek, A review of vessel extraction techniques and algorithms. ACM Comput. Surv., 36 (2): 81–121, 2004.
[16] A. Lefohn, J. Kniss, C. Hansen, and R. Whitaker, Interactive deformation and visualization of level set surfaces using graphics hardware. In Proceedings of IEEE Visualization, pages 75–82, 2003.
[17] J. H. Macke, N. Maack, R. Gupta, W. Denk, B. Schölkopf, and A. Borst Contour-propagation algorithms for semi-automated reconstruction of neural processes. Journal of Neuroscience Methods, 167 (2): 349–357, 2008.
[18] M. Maire, P. Arbelaez, C. Fowlkes, and J. Malik, Using contours to detect and localize junctions in natural images. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR'08), pages 1–8, 2008.
[19] D. Martin, C. Fowlkes, and J. Malik, Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. on Pattern Analysis and Machine Intelligence, 26 (1): 530–549, 2004.
[20] D. Mayerich, L. Abbott, and J. Keyser, Visualization of cellular and microvascular relationships. IEEE Transactions on Visualization and Computer Graphics, 14 (6): 1611–1618, 2008.
[21] Y. Mishchenko Automation of 3d reconstruction of neural tissue from large volume of conventional serial section transmission electron micro-graphs. Journal of Neuroscience Methods, 176: 276–289, 2009.
[22] P. Perona and J. Malik, Scale space and edge detection using anisotropic diffusion. In IEEE Trans. in Pattern Analysis and Machine Intelligence, volume 12, pages 629–639, 1990.
[23] V. Petrovic, J. Fallon, and F. Kuester, Visualizing whole-brain dti tractography with gpu-based tuboids and lod management. IEEE Trans. Vis. Comput. Graph., 13 (6): 1488–1495, 2007.
[24] G. Reina, K. Bidmon, F. Enders, P. Hastreiter, and T. Ertl GPU-Based Hyperstreamlines for Diffusion Tensor Imaging. In Proceedings of EUROGRAPHICS - IEEE VGTC Symposium on Visualization 2006, pages 35–42, 2006.
[25] H. Scharsach, M. Hadwiger, A. Neubauer, and K. Bühler, Perspective iso-surface and direct volume rendering for virtual endoscopy applications. In Eurovis 2006, pages 315–322, 2006.
[26] J. Sethian Level set methods and fast marching methods. Cambridge University Press, 2002.
[27] S. J. Smith Circuit reconstruction tools today. Current Opinion in Neurobiology, 17 (5): 601–608, October 2007.
[28] O. Sporns, G. Tononi, and R. Kötter The human connectome: A structural description of the human brain. PLoS Computational Biology, 1 (4): e42+, September 2005.
[29] T. Tasdizen, S. Awate, R. Whitaker, and N. Foster MRI tissue classification with neighborhood statistics: A nonparametric, entropy-minimizing approach. In MICCAI 2005, pages 517–525, 2005.
[30] T. Tasdizen, R. Whitaker, R. Marc, and B. Jones Enhancement of cell boundaries in transmission micropscopy images. In IEEE International Conf. on Image Processing (ICIP '05), volume 2, pages 129–132, 2005.
[31] C. Tomasi, and R. Manduchi, Bilateral filtering for gray and color images. In ICCV '98, pages 839–846, 1998.
[32] A. Vazquez-Reina, E. Miller, and H. Pfister Multiphase geometric couplings for the segmentation of neural processes. In, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2020–2027, 2009.
18 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool