3D segmentation of cell boundaries from whole cell cryogenic electron tomography volumes

Journal of Structural Biology

Volumn 170, Issue 1, 2010, Pages 134-145

  Moussavi, Farshid ^a   Heitz, Geremy ^a   Amat, Fernando ^a   Comolli, Luis R ^b   Koller, Daphne ^a   Horowitz, Mark ^a  

^a Stanford University   (United States)

^b LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

Author keywords

Biomedical image processing; Computer vision; Electron tomography; Object detection; Probabilistic inference; Segmentation

Indexed keywords

ACCURACY; ARTICLE; AUTOMATION; CAULOBACTER CRESCENTUS; CELL MEMBRANE; CELL STRUCTURE; CLASSIFICATION ALGORITHM; COMPUTER SIMULATION; DATA ANALYSIS SOFTWARE; ELECTRON TOMOGRAPHY; HIGH THROUGHPUT SCREENING; IMAGE PROCESSING; IMAGE QUALITY; MATHEMATICAL MODEL; NONHUMAN; PRIORITY JOURNAL; STRUCTURE ANALYSIS; THREE DIMENSIONAL IMAGING; TOMOGRAPHY; WHOLE CELL;

ALGORITHMS; CELL MEMBRANE; CRYOELECTRON MICROSCOPY; ELECTRON MICROSCOPE TOMOGRAPHY;

EID: 77950516436     PISSN: 10478477     EISSN: 10958657     Source Type: Journal    
DOI: 10.1016/j.jsb.2009.12.015     Document Type: Article

References (54)

1. Amat F., Moussavi F., Comolli L.R., Elidan G., Downing K.H., Horowitz M. Markov random field based automatic image alignment for electron tomography. J. Struct. Biol. 2007, 161(3):260-275.
2. Bartesaghi A., Sapiro G., Subramaniam S. An energy-based three-dimensional segmentation approach for the quantitative interpretation of electron tomograms. IEEE Trans. Image Process. 2005, 14(9):1314-1323.
3. Baumeister W. Electron tomography: towards visualizing the molecular organization of the cytoplasm. Curr. Opin. Struct. Biol. 2002, 12:679-684.
4. Borgefors G. Hierarchical chamfer matching: a parametric edge matching algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 1988, 10(6):849-865.
5. Bowman G.R., Comolli L.R., Zhu J., Eckart M., Koenig M., Downing K.H., Moerner W., Earnest T., Shapiro L. A polymeric protein anchors the chromosomal origin/parb complex at a bacterial cell pole. Cell 2008, 134(6):945-955.
6. Bregler, C., Slaney, M., 1995. Snakes technical report. Available from: <>. http://web.interval.com/papers/1995-017/.
7. Canny J. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 1986, 8(6):679-698.
8. Chan T.F., Vese L.A. Active contours without edges. IEEE Trans. Image Process. 2001, 10(2):266-277.
9. Comolli L.R., Baker B.J., Downing K.H., Siegerist C.E., Banfield J.F. Three-dimensional analysis of the structure and ecology of a novel, ultra-small archaeon. ISME J 2008, 3(2):159-167.
10. Cootes, T., Taylor, C., 1993. Active shape model search using local grey-level models: a quantitative evaluation. In: 4th British Machine Vision Conference. BMVA Press, pp. 639-648.
11. Derpanis, K.G., 2009. York university center for vision research software page. Available from: <>. http://cvr.yorku.ca/members/gradstudents/kosta/SoftwareDemos/#level_set.
12. Dey, T.K., Giesen, J., 2001. Detecting undersampling in surface reconstruction. In: SCG '01: Proceedings of the Seventeenth Annual Symposium on Computational Geometry. ACM, New York, NY, USA, pp. 257-263.
13. Dey, T.K., Sun, J., 2005. An adaptive mls surface for reconstruction with guarantees. In: SGP '05: Proceedings of the Third Eurographics Symposium on Geometry Processing. Eurographics Association, Aire-la-Ville, Switzerland, p. 43.
14. Fernandez J., Sanjurjo J., Carazo J. A spectral estimation approach to contrast transfer function detection in electron microscopy. Ultramicroscopy 1997, 267-295(29).
15. Ferrari V., Fevrier L., Jurie F., Schmid C. Groups of adjacent contour segments for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 2008, 30(1):36-51.
16. Fitzgibbon A.W., Pilu M., Fisher R.B. Direct least square fitting of ellipses. IEEE Trans. Pattern Anal. Mach. Intell. 1999, 21(5):476-480.
17. Fowlkes, C.C., Martin, D.R., Malik, J., 2005. The berkeley segmentation engine (bse). Available from: <>. http://www.cs.berkeley.edu/fowlkes/BSE/.
18. Frangakis A.S., Hegerl R. Segmentation of two- and three-dimensional data from electron microscopy using eigenvector analysis. J. Struct. Biol. 2002, 138(1-2):105-113.
19. Frank J. Electron Tomography 2006, Springer-Verlag.
20. Friedman J., Hastie T., Tibshirani R. Additive logistic regression: a statistical view of boosting. Ann. Stat. 2000, 28(2):337-374.
21. Gan L., Chen S., Jensen G.J.J. Molecular organization of gram-negative peptidoglycan. Proc. Natl. Acad. Sci. Unit. States Am. 2008, 105(48):18953-18957.
22. Garduño E., Wong-Barnum M., Volkmann N., Ellisman M. Segmentation of electron tomographic data sets using fuzzy set theory principles. J. Struct. Biol. 2008, 162(3):368-379.
23. Heitz G., Elidan G., Packer B., Koller D. Shape-based object localization for descriptive classification. Int. J. Comput. Vis. 2009, 84(1):40-62.
24. Heymann J.B., Belnap D.M. Bsoft: image processing and molecular modeling for electron microscopy. J. Struct. Biol. 2007, 157(1):3-18. (software tools for macromolecular microscopy).
25. Hochbaum D.S. An efficient algorithm for image segmentation, markov random fields and related problems. J. ACM 2001, 48(4):686-701.
26. Kak A., Slaney M. Principles of Computerized Tomographic Imaging 2001, Society for Industrial and Applied Mathematics, Philadelphia, PA, USA.
27. Kass M., Witkin A., Terzopoulos D. Snakes: active contour models. Int. J. Comput. Vis. 1988, V1(4):321-331.
28. Kim, J., Zabih, R., 2003. A segmentation algorithm for contrast-enhanced images. In: IEEE International Conference on Computer Vision, vol. 1, pp. 502-509.
29. Koster A., Klumperman J. Electron microscopy in cell biology: integrating structure and function. Nat. Rev. Mol. Cell. Biol. 2003, 4:6-10.
30. Lafferty, J., McCallum, A., Pereira, F., 2001. Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proc. 18th International Conference on Machine Learning. Morgan Kaufmann, San Francisco, CA, pp. 282-289.
31. Lawrence A., Bouwer J., Perkins G., Ellisman M. Transform-based backprojection for volume reconstruction of large format electron microscope tilt series. J. Struct. Biol. 2006, 154(2):144-167.
32. Li, C., Kao, C., Gore, J., Ding, Z., 2007. Implicit active contours driven by local binary fitting energy. In: IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recogn. IEEE Computer Society, Washington, DC, USA, pp. 1-7.
33. Martin D.R., Fowlkes C.C., Malik J. Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 2004, 26(5):530-549.
34. Mastronarde D.N. Automated electron microscope tomography using robust prediction of specimen movements. J. Struct. Biol. 2005, 152(1):36-51.
35. Meyer F. An overview of morphological segmentation. IJPRAI 2001, 15(7):1089-1118.
36. Mumford D., Shah J. Optimal approximations by piecewise smooth functions and associated variational problems. Comm. Pure. Appl. Math. 1989, 42(5):577-685.
37. Natterer F. The Mathematics of Computerized Tomography 2001, Society for Industrial and Applied Mathematics, Philadelphia, PA, USA.
38. Nguyen H., Ji Q. Shape-driven three-dimensional watersnake segmentation of biological membranes in electron tomography. IEEE Trans. Med. Imaging 2008, 27(5):616-628.
39. Nguyen H.T., Worring M., van den Boomgaard R. Watersnakes: energy-driven watershed segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2003, 25(3):330-342.
40. Opelt A., Pinz A., Zisserman A. Learning an alphabet of shape and appearance for multi-class object detection. Int. J. Comput. Vis. 2008, 80(1):16-44.
41. Ortiz J., Foerster F., Kuerner J., Linaroudis A., Baumeister W. Mapping 70s ribosomes in intact cells by cryoelectron tomography and pattern recognition. J. Struct. Biol. 2006, 156(2):334-341.
42. Pruggnaller S., Mayr M., Frangakis A.S. A visualization and segmentation toolbox for electron microscopy. J. Struct. Biol. 2008, 164(1):161-165.
43. Sandberg K. Methods for image segmentation in cellular tomography. Cellular Electron Microscopy of Methods in Cell Biology 2007, vol. 79:769-798. Academic Press.
44. Sandberg K., Brega M. Segmentation of thin structures in electron micrographs using orientation fields. J. Struct. Biol. 2007, 157(2):403-415.
45. Sethian, J.A., 1995. A fast marching level set method for monotonically advancing fronts. In: Proc. Nat. Acad. Sci., pp. 1591-1595.
46. Shi J., Malik J. Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22(8):888-905.
47. Sobel, I.E., 1970. Camera models and machine perception. Ph.D. thesis. Stanford University, Stanford, CA, USA.
48. Suloway C., Pulokas J., Fellmann D., Cheng A., Guerra F., Quispe J., Stagg S., Potter C.S., Carragher B. Automated molecular microscopy: the new leginon system. J. Struct. Biol. 2005, 151(1):41-60.
49. Vincent L., Soille P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 1991, 13(6):583-598.
50. Volkmann N. A novel three-dimensional variant of the watershed transform for segmentation of electron density maps. J. Struct. Biol. 2002, 138(1-2):123-129.
51. Wah B. Wiley Encyclopedia Of Computer Science And Engineering 2007, John Wiley & Sons.
52. Zabih, R., Kolmogorov, V., 2004. Spatially coherent clustering using graph cuts. In: IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recogn., pp. 437-444.
53. Zheng S.Q., Keszthelyi B., Branlund E., Lyle J.M., Braunfeld M.B., Sedat J.W., Agard D.A. Ucsf tomography: an integrated software suite for real-time electron microscopic tomographic data collection, alignment, and reconstruction. J. Struct. Biol. 2007, 157(1):138-147.
54. Ziou D., Tabbone S. Edge detection techniques - an overview. Int. J. Pattern Recogn. Image Anal. 1998, 8:537-559.