SegEM: Efficient Image Analysis for High-Resolution Connectomics

Neuron

Volumn 87, Issue 6, 2015, Pages 1193-1206

  Berning, Manuel ^a   Boergens, Kevin M ^a   Helmstaedter, Moritz ^a  

^a MAX PLANCK INSTITUTE FOR BRAIN RESEARCH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMAL TISSUE; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; BRAIN CORTEX; CLASSIFIER; CONNECTOME; CONTROLLED STUDY; ELECTRON MICROSCOPY; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGE QUALITY; IMAGE RECONSTRUCTION; MACHINE LEARNING; MOUSE; NERVE CELL NETWORK; NEUROSCIENCE; NONHUMAN; PRIORITY JOURNAL; RETINA; SYNAPTIC TRANSMISSION; ANIMAL; CYTOLOGY; HUMAN; LABORATORY AUTOMATION; PHYSIOLOGY; PROCEDURES; THREE DIMENSIONAL IMAGING; ULTRASTRUCTURE; VISUAL CORTEX;

ANIMALS; AUTOMATION, LABORATORY; CONNECTOME; HUMANS; IMAGING, THREE-DIMENSIONAL; MICE; MICROSCOPY, ELECTRON; RETINA; VISUAL CORTEX;

EID: 84942133423     PISSN: 08966273     EISSN: 10974199     Source Type: Journal    
DOI: 10.1016/j.neuron.2015.09.003     Document Type: Article

References (34)

1. Andres B., Koethe U., Kroeger T., Helmstaedter M., Briggman K.L., Denk W., Hamprecht F.A. 3D segmentation of SBFSEM images of neuropil by a graphical model over supervoxel boundaries. Med. Image Anal. 2012, 16:796-805.
2. Andres B., Kroeger T., Briggman K., Denk W., Korogod N., Knott G., Koethe U., Hamprecht F. Globally Optimal Closed-Surface Segmentation for Connectomics. Computer Vision - ECCV 2012 2012, Springer Berlin Heidelberg. A. Fitzgibbon, S. Lazebnik, P. Perona, Y. Sato, C. Schmid (Eds.).
3. Bergstra, J., Yamins, D., and Cox, D.D. Hyperopt: A Python library for optimizing the hyperparameters of machine learning algorithms. Proceedings of the 12th Python in Science Conference, 2013. 13-20.
4. Briggman K.L., Bock D.D. Volume electron microscopy for neuronal circuit reconstruction. Curr. Opin. Neurobiol. 2012, 22:154-161.
5. Briggman K.L., Helmstaedter M., Denk W. Wiring specificity in the direction-selectivity circuit of the retina. Nature 2011, 471:183-188.
6. Cardona A., Saalfeld S., Schindelin J., Arganda-Carreras I., Preibisch S., Longair M., Tomancak P., Hartenstein V., Douglas R.J. TrakEM2 software for neural circuit reconstruction. PLoS ONE 2012, 7:e38011.
7. Ciresan D., Giusti A., Gambardella L.M., Schmidhuber J. Deep neural networks segment neuronal membranes in electron microscopy images. Adv. Neural Inf. Process. Syst. 2012, 25:2843-2851.
8. Denk W., Horstmann H. Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure. PLoS Biol. 2004, 2:e329.
9. Denk W., Briggman K.L., Helmstaedter M. Structural neurobiology: missing link to a mechanistic understanding of neural computation. Nat. Rev. Neurosci. 2012, 13:351-358.
10. Funke, J., Andres, B., Hamprecht, F.A., Cardona, A., and Cook, M. Efficient automatic 3D-reconstruction of branching neurons from EM data. Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, 16-21 June 2012 2012. 1004-1011.
11. Hayworth K., Kasthuri N., Schalek R., Lichtman J. Automating the collection of ultrathin serial sections for large volume TEM reconstructions. Microsc. Microanal. 2006, 12:86-87.
12. Helmstaedter M. Cellular-resolution connectomics: challenges of dense neural circuit reconstruction. Nat. Methods 2013, 10:501-507.
13. Helmstaedter M., Briggman K.L., Denk W. High-accuracy neurite reconstruction for high-throughput neuroanatomy. Nat. Neurosci. 2011, 14:1081-1088.
14. Helmstaedter M., Briggman K.L., Turaga S.C., Jain V., Seung H.S., Denk W. Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 2013, 500:168-174.
15. Jain, V., Murray, J.F., Roth, F., Turaga, S., Zhigulin, V., Briggman, K.L., Helmstaedter, M.N., Denk, W., and Seung, H.S. Supervised learning of image restoration with convolutional networks. Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, 2007. IEEE, 1-8.
16. Jain, V., Bollmann, B., Richardson, M., Berger, D.R., Helmstaedter, M.N., Briggman, K.L., Denk, W., Bowden, J.B., Mendenhall, J.M., and Abraham, W.C. Boundary learning by optimization with topological constraints. Computer Vision and Pattern Recognition (CVPR), (2010a) IEEE Conference on, 2010. IEEE, 2488-2495.
17. Jain V., Seung H.S., Turaga S.C. Machines that learn to segment images: a crucial technology for connectomics. Curr. Opin. Neurobiol. 2010, 20:653-666.
18. Jain V., Turaga S.C., Briggman K.L., Helmstaedter M.N., Denk W., Seung H.S. Learning to Agglomerate Superpixel Hierarchies. Adv. Neural Inf. Process. Syst. 2011, 24:2.
19. Jones C., Liu T., Cohan N.W., Ellisman M., Tasdizen T. Efficient semi-automatic 3D segmentation for neuron tracing in electron microscopy images. J. Neurosci. Methods 2015, 246:13-21.
20. Kasthuri N., Hayworth K.J., Berger D.R., Schalek R.L., Conchello J.A., Knowles-Barley S., Lee D., Vázquez-Reina A., Kaynig V., Jones T.R., et al. Saturated Reconstruction of a Volume of Neocortex. Cell 2015, 162:648-661.
21. Kaynig V., Vazquez-Reina A., Knowles-Barley S., Roberts M., Jones T.R., Kasthuri N., Miller E., Lichtman J., Pfister H. Large-scale automatic reconstruction of neuronal processes from electron microscopy images. Med. Image Anal. 2015, 22:77-88.
22. Knott G., Marchman H., Wall D., Lich B. Serial section scanning electron microscopy of adult brain tissue using focused ion beam milling. J. Neurosci. 2008, 28:2959-2964.
23. Lichtman J.W., Denk W. The big and the small: challenges of imaging the brain's circuits. Science 2011, 334:618-623.
24. Liu T., Jones C., Seyedhosseini M., Tasdizen T. A modular hierarchical approach to 3D electron microscopy image segmentation. J. Neurosci. Methods 2014, 226:88-102.
25. Nunez-Iglesias J., Kennedy R., Plaza S.M., Chakraborty A., Katz W.T. Graph-based active learning of agglomeration (GALA): a Python library to segment 2D and 3D neuroimages. Front. Neuroinform. 2014, 8:34.
26. Saalfeld S., Cardona A., Hartenstein V., Tomančák P. CATMAID: collaborative annotation toolkit for massive amounts of image data. Bioinformatics 2009, 25:1984-1986.
27. Seyedhosseini M., Kumar R., Jurrus E., Giuly R., Ellisman M., Pfister H., Tasdizen T. Detection of neuron membranes in electron microscopy images using multi-scale context and radon-like features. Med Image Comput Comput Assist Interv 2011, 14:670-677.
28. Seyedhosseini, M., Sajjadi, M., and Tasdizen, T. Image segmentation with cascaded hierarchical models and logistic disjunctive normal networks. Computer Vision (ICCV), 2013 IEEE International Conference on, 2013. IEEE, 2168-2175.
29. Snoek J., Larochelle H., Adams R.P. Practical Bayesian optimization of machine learning algorithms. Adv. Neural Inf. Process. Syst. 2012, 25:2951-2959.
30. Sommer, C., Straehle, C., Kothe, U., and Hamprecht, F.A. Ilastik: Interactive learning and segmentation toolkit. Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on, March 30 2011-April 2 2011 2011. 230-233.
31. Takemura S.-Y., Bharioke A., Lu Z., Nern A., Vitaladevuni S., Rivlin P.K., Katz W.T., Olbris D.J., Plaza S.M., Winston P., et al. A visual motion detection circuit suggested by Drosophila connectomics. Nature 2013, 500:175-181.
32. Turaga, S.C., Briggman, K.L., Helmstaedter, M., Denk, W., and Seung, H.S. 2009. Maximin affinity learning of image segmentation. arXiv preprint arXiv:0911.5372.
33. Turaga S.C., Murray J.F., Jain V., Roth F., Helmstaedter M., Briggman K., Denk W., Seung H.S. Convolutional networks can learn to generate affinity graphs for image segmentation. Neural Comput. 2010, 22:511-538.
34. Vazquez-Reina, A., Gelbart, M., Huang, D., Lichtman, J., Miller, E., and Pfister, H. Segmentation fusion for connectomics. Computer Vision (ICCV), 2011 IEEE International Conference on, 2011. IEEE, 177-184.