Biologically inspired EM image alignment and neural reconstruction

Bioinformatics

Volumn 27, Issue 16, 2011, Pages 2216-2223

  Knowles Barley, Seymour ^a   Butcher, Nancy J ^b   Meinertzhagen, Ian A ^b   Armstrong, J Douglas ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

^b DALHOUSIE UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ANIMAL; ARTICLE; CELL MEMBRANE; DROSOPHILA; METHODOLOGY; NERVE CELL; THREE DIMENSIONAL IMAGING; TRANSMISSION ELECTRON MICROSCOPY; ULTRASTRUCTURE;

ALGORITHMS; ANIMALS; CELL MEMBRANE; DROSOPHILA; IMAGING, THREE-DIMENSIONAL; MICROSCOPY, ELECTRON, TRANSMISSION; NEURONS;

EID: 79961190794     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btr378     Document Type: Article

References (43)

1. Anderson,J.R. et al. (2009) A computational framework for ultrastructural mapping of neural circuitry. PLoS Biol., 7, e1000074, 0493-0512.
2. Andres,B. et al. (2008) Segmentation of SBFSEM volume data of neural tissue by hierarchical classification. In Rigoll,G. (ed) Pattern Recognition, Vol. 5096 of Lecture Notes in Computer Science, chapter 15. Springer, Berlin, Heidelberg, pp. 142-152.
3. Angelucci,A. et al. (2002) Anatomical origins of the classical receptive field and modulatory surround field of single neurons in macaque visual cortical area V1. Progr. Brain Res., 136, 373-388.
4. Beucher,S. (1991) The watershed transformation applied to image segmentation. Scanning Microscopy International, 6, 299-314.
5. Briggman,K.L. and Denk,W. (2006) Towards neural circuit reconstruction with volume electron microscopy techniques. Curr. Opin. Neurobiol., 16, 562-570.
6. Carandini,M. et al. (2005) Do we know what the early visual system does? J. Neurosci., 25, 10577-10597.
7. Cardona,A. et al. (2010a) An integrated micro- and macroarchitectural analysis of the Drosophila brain by computer-assisted serial section electron microscopy. PLoS Biol., 8, e1000502, 1-17.
8. Cardona,A. et al. (2010b) Identifying neuronal lineages of Drosophila by sequence analysis of axon tracts. J. Neurosci., 30, 7538-7553.
9. Chklovskii,D.B. et al. (2010) Semi-automated reconstruction of neural circuits using electron microscopy. Curr. Opin. Neurobiol., 20, 667-675.
10. Eisenstein,M. (2009) Neural circuits: putting neurons on the map. Nature, 461, 1149-1152.
11. Fiala,J.C. (2005) Reconstruct: a free editor for serial section microscopy. J. Microsc., 218 (Pt 1), 52-61.
12. Fitzpatrick,D. (2000) Seeing beyond the receptive field in primary visual cortex. Curr. Opin. Neurobiol., 10, 438-443.
13. Hartline,H.K. et al. (1956) Inhibition in the eye of Limulus. J. Gen. Physiol., 39, 651-673.
14. Hubel,D.H. and Wiesel,T.N. (1959) Receptive fields of single neurones in the cat's striate cortex. J. Physiol., 148, 574-591.
15. Ichida,J.M. et al. (2007) Response facilitation from the 'Suppressive' receptive field surround of Macaque V1 neurons. J. Neurophysiol., 98, 2168-2181.
16. Jain,V. et al. (2010) Machines that learn to segment images: a crucial technology for connectomics. Curr. Opin. Neurobiol., 20, 653-666.
17. Jiang,X. et al. (2002) Curve morphing by weighted mean of strings. In IEEE 16th International Conference on Pattern Recognition, vol. 4, IEEE Press, pp. 192-195.
18. Jurrus,E. et al. (2008) An optimal-path approach for neural circuit reconstruction. In IEEE International Symposium on Biomedical Imaging: From Nano to Macro., vol. 2008, IEEE Press, pp. 1609-1612.
19. Kaynig,V. et al. (2010) Neuron geometry extraction by perceptual grouping in ssTEM images. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Press, pp. 2902-2909.
20. Kremer,J.R. et al. (1996) Computer visualization of three-dimensional image data using IMOD. J. Struct. Biol., 116, 71-76.
21. Leiss,F. et al. (2009) Synaptic organization in the adult Drosophila mushroom body calyx. J. Comp. Neurol., 517, 808-824.
22. Lichtman,J.W. and Sanes,J.R. (2008) Ome sweet ome: what can the genome tell us about the connectome? Curr. Opin. Neurobiol., 18, 346-353.
23. Lorenz,C. et al. (1997) A multi-scale line filter with automatic scale selection based on the Hessian Matrix for medical image segmentation. In Proceedings of the First International Conference on Scale-Space Theory in Computer Vision, SCALE-SPACE '97. Springer, London, UK, pp. 152-163.
24. Meinertzhagen,I.A. and O'Neil,S.D. (1991) Synaptic organization of columnar elements in the lamina of the wild type in Drosophila melanogaster. J. Comp. Neurol., 305, 232-263.
25. Mishchenko,Y. (2009) Automation of 3D reconstruction of neural tissue from large volume of conventional serial section transmission electron micrographs. J. Neurosci. Methods, 176, 276-289.
26. Needleman,S.B. and Wunsch,C.D. (1970) A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol., 48, 443-453.
27. O'Carroll,D. (1993) Feature-detecting neurons in dragonflies. Nature, 362, 541-543.
28. Olshausen,B.A. and Field,D.J. (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381, 607-609.
29. Olshausen,B.A. and Field,D.J. (2005) How close are we to understanding V1? Neural Comput., 17, 1665-1699.
30. Ozeki,H. et al. (2004) Relationship between excitation and inhibition underlying size tuning and contextual response modulation in the cat primary visual cortex. J. Neurosci., 24, 1428-1438.
31. Ozeki,H. et al. (2009) Inhibitory stabilization of the cortical network underlies visual surround suppression. Neuron, 62, 578-592.
32. Palagina,G. et al. (2009) Strengthening of lateral activation in adult rat visual cortex after retinal lesions captured with voltage-sensitive dye imaging in vivo. Proc. Natl Acad. Sci. USA, 106, 8743-8747.
33. Rand,W.M. (1971) Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc., 66, 846-850.
34. Saalfeld,S. et al. (2010) As-rigid-as-possible mosaicking and serial section registration of large ssTEM datasets. Bioinformatics, 26, i57-i63.
35. Smith,S.J. (2007) Circuit reconstruction tools today. Curr. Opin. Neurobiol., 17, 601-608.
36. Smith,T.F. and Waterman,M.S. (1981) Identification of common molecular subsequences. J. Mol. Biol., 147, 195-197.
37. Sommer,C. et al. (2011) ilastik: Interactive Learning and Segmentation Toolkit. In IEEE International Symposium on Biomedical Imaging: From Nano toMacro, 2011 IEEE International Symposium on. IEEE Press, pp. 230-233.
38. Takemura,S.-Y.Y. et al. (2008) Synaptic circuits of the Drosophila optic lobe: the input terminals to the medulla. J. Comp. Neurol., 509, 493-513.
39. Turaga,S.C. et al. (2009) Maximin affinity learning of image segmentation. In Neural Information Processing Systems (NIPS). Vol. 22, NIPS Foundation, pp. 1865-1873.
40. Unnikrishnan,R. et al. (2007) Toward objective evaluation of image segmentation algorithms. IEEE Trans. Pattern Anal. Mach. Intell., 29, 929-944.
41. Venkataraju,K.U. et al. (2009) Automatic markup of neural cell membranes using boosted decision stumps. In Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging, pp. 1039-1042.
42. White,J.G. et al. (1976) The structure of the ventral nerve cord of Caenorhabditis elegans. Philos. Trans. R. Soc. London Ser. B Biol. Sci., 275, 327-348.
43. Ziou,D. (1991) Line detection using an optimal IIR filter. Pattern Recognit., 24, 465-478.