Abstract
We present a novel semi-automatic method for segmenting neural processes in large, highly anisotropic EM (electron microscopy) image stacks. Our method takes advantage of sparse scribble annotations provided by the user to guide a 3D variational segmentation model, thereby allowing our method to globally optimally enforce 3D geometric constraints on the segmentation. Moreover, we leverage a novel algorithm for propagating segmentation constraints through the image stack via optimal volumetric pathways, thereby allowing our method to compute highly accurate 3D segmentations from very sparse user input. We evaluate our method by reconstructing 16 neural processes in a 1024×1024×50 nanometer-scale EM image stack of a mouse hippocampus. We demonstrate that, on average, our method is 68% more accurate than previous state-of-the-art semi-automatic methods.
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Andres, B., Köthe, U., Helmstaedter, M., Denk, W., Hamprecht, F.A.: Segmentation of SBFSEM volume data of neural tissue by hierarchical classification. In: Rigoll, G. (ed.) DAGM 2008. LNCS, vol. 5096, pp. 142–152. Springer, Heidelberg (2008)
Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. Int. J. Comp. Vis. 70(2), 109–131 (2006)
Bradski, G.: The OpenCV Library. Dr. Dobb’s Journal of Software Tools (2000)
Chklovskii, D.B., Vitaladevuni, S., Scheffer, L.K.: Semi-Automated reconstruction of neural circuits using electron microscopy. Curr. Opin. Neurobiol. 20(5), 667–675 (2010)
Farnebäck, G.: Polynomial Expansion for Orientation and Motion Estimation. Ph.D. thesis, Linköping University, Sweden (2002)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Inc., Englewood Cliffs (2006)
Jain, V., Bollmann, B., Richardson, M., Berger, D.R., Helmstaedter, M.N., Briggman, K.L., Denk, W., Bowden, J.B., Mendenhall, J.M., Abraham, W.C., Harris, K.M., Kasthuri, N., Hayworth, K.J., Schalek, R., Tapia, J.C., Lichtman, J.W., Seung, H.S.: Boundary learning by optimization with topological constraints. In: IEEE CVPR, pp. 2488–2495 (2010)
Jeong, W.K., Beyer, J., Hadwiger, M., Vazquez-Reina, A., Pfister, H., Whitaker, R.T.: Scalable and interactive segmentation and visualization of neural processes in EM datasets. IEEE Trans. Vis. Comp. Graph. 15(6), 1505–1514 (2009)
Jeong, W.K., Fletcher, P.T., Tao, R., Whitaker, R.T.: Interactive visualization of volumetric white matter connectivity in DT-MRI using a parallel-hardware Hamilton-Jacobi solver. IEEE Trans. Vis. Comp. Graph. 13(6), 1480–1487 (2007)
Jurrus, E., Whitaker, R., Jones, B.W., Marc, R., Tasdizen, T.: An optimal-path approach for neural circuit reconstruction. In: ISBI, pp. 1609–1612 (2008)
Kaynig, V., Fuchs, T.J., Buhmann, J.M.: Geometrical consistent 3D tracing of neuronal processes in ssTEM data. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6362, pp. 209–216. Springer, Heidelberg (2010)
Kaynig, V., Fuchs, T., Buhmann, J.M.: Neuron geometry extraction by perceptual grouping in ssTEM images. In: IEEE CVPR, pp. 2902–2909 (2010)
Lucchi, A., Smith, K., Achanta, R., Lepetit, V., Fua, P.: A fully automated approach to segmentation of irregularly shaped cellular structures in EM images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6362, pp. 463–471. Springer, Heidelberg (2010)
Macke, J.H., Maack, N., Gupta, R., Denk, W., Schlkopf, B., Borst, A.: Contour-propagation algorithms for semi-automated reconstruction of neural processes. J. Neurosci. Methods 167(2), 349–357 (2008)
Mishchenko, Y.: Automation of 3D reconstruction of neural tissue from large volume of conventional serial section transmission electron micrographs. J. Neurosci. Methods 176(2), 276–289 (2009)
Pan, Y., Jeong, W.K., Whitaker, R.T.: Markov surfaces: A probabilistic framework for user-assisted three-dimensional image segmentation. In: PMMIA, pp. 57–68 (2009)
Unger, M., Mauthner, T., Pock, T., Bischof, H.: Tracking as segmentation of spatial-temporal volumes by anisotropic weighted TV. In: EMMCVPR, pp. 193–206 (2008)
Unger, M., Pock, T., Bischof, H.: Continuous globally optimal image segmentation with local constraints. In: CVWW (2008)
Vázquez-Reina, A., Miller, E., Pfister, H.: Multiphase geometric couplings for the segmentation of neural processes. In: IEEE CVPR, pp. 2020–2027 (2009)
Venkataraju, K.U., Paiva, A.R.C., Jurrus, E., Tasdizen, T.: Automatic markup of neural cell membranes using boosted decision stumps. In: ISBI, pp. 1039–1042 (2009)
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Roberts, M. et al. (2011). Neural Process Reconstruction from Sparse User Scribbles. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6891. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23623-5_78
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DOI: https://doi.org/10.1007/978-3-642-23623-5_78
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