68. M. Kunz, Z. Yu, A.S. Frangakis, M-free: mask-independent scoring of the reference bias.
J. Struct. Biol. 192, 307–311 (2015)
69. Z. Yu, A.S. Frangakis, M-free: scoring the reference bias in sub-tomogram averaging and
template matching. J. Struct. Biol. 187, 10–19 (2014)
70. X.P. Xu, C. Page, N. Volkmann, Efficient extraction of macromolecular complexes from
electron tomograms based on reduced representation templates. Lect. Notes Comput. Sci.
9256, 423–431 (2015)
71. N. Volkmann, An approach to automated particle picking from electron micrographs based
on reduced representation templates. J. Struct. Biol. 145, 152–156 (2004)
72. J. Pierson, M. Vos, J.R. McIntosh, P.J. Peters, Perspectives on electron cryo-tomography of
vitreous cryo-sections. J. Electron. Microsc. (Tokyo) 60(Suppl 1), S93–S100 (2011)
73. A. Buades, B. Coll, J.-M. Morel, A non-local algorithm for image denoising. IEEE Comput.
Soc. Conf. Comp. Vis. Pattern Recog. 2, 60–65 (2005)
74. C.M. Spahn, E. Jan, A. Mulder, R.A. Grassucci, P. Sarnow, J. Frank, Cryo-EM visualization
of a viral internal ribosome entry site bound to human ribosomes: the IRES functions as an
RNA-based translation factor. Cell 118, 465–475 (2004)
75. J. Wu, B. Rajwa, D.L. Filmer, C.M. Hoffmann, B. Yuan, C. Chiang, J. Sturgis,
J.P. Robinson, Automated quantification and reconstruction of collagen matrix from 3D
confocal datasets. J. Microsc. 210, 158–165 (2003)
76. A.M. Stein, D.A. Vader, L.M. Jawerth, D.A. Weitz, L.M. Sander, An algorithm for
extracting the network geometry of three-dimensional collagen gels. J. Microsc. 232, 463–
475 (2008)
77. G. Herberich, R. Windoffer, R. Leube, T. Aach, 3D segmentation of keratin intermediate
filaments in confocal laser scanning microscopy, in Proceedings of Annual International
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78. S. Basu, C. Liu, G.K. Rohde, Localizing and extracting filament distributions from
microscopy images. J. Microsc. 258, 13–23 (2015)
79. M. Alioscha-Perez, C. Benadiba, K. Goossens, S. Kasas, G. Dietler, R. Willaert, H. Sahli,
A robust actin filaments image analysis framework. PLoS Comput. Biol. 12, e1005063 (2016)
80. W. Mickel, S. Münster, L.M. Jawerth, D.A. Vader, D.A. Weitz, A.P. Sheppard, K. Mecke,
B. Fabry, G.E. Schröder-Turk, Robust pore size analysis of filamentous networks from
three-dimensional confocal microscopy. Biophys. J. 95, 6072–6080 (2008)
81. B. Eltzner, C. Wollnik, C. Gottschlich, S. Huckemann, F. Rehfeldt, The filament sensor for
near real-time detection of cytoskeletal fiber structures. PLoS ONE 10, e0126346 (2015)
82. L.A. Loss, G. Bebis, B. Parvin, Iterative tensor voting for perceptual grouping of ill-defined
curvilinear structures. IEEE Trans. Med. Imaging 30, 1503–1513 (2011)
83. M. Beil, H. Braxmeier, F. Fleischer, V. Schmidt, P. Walther, Quantitative analysis of keratin
filament networks in scanning electron microscopy images of cancer cells. J. Microsc. 220,
84–95 (2005)
84. S. Lück, M. Sailer, V. Schmidt, P. Walther, Three-dimensional analysis of intermediate
filament networks using SEM tomography. J. Microsc. 239, 1–16 (2010)
85. D. Mayerich, J. Keyser, Hardware accelerated segmentation of complex volumetric filament
networks. IEEE Trans. Vis. Comput. Graph. 15, 670–681 (2009)
86. T. Xu, D. Vavylonis, X. Huang, 3D actin network centerline extraction with multiple active
contours. Med. Image Anal. 18, 272–284 (2014)
87. T. Xu, D. Vavylonis, F.C. Tsai, G.H. Koenderink, W. Nie, E. Yusuf, I-Ju Lee, J.Q. Wu,
X. Huang, SOAX: a software for quantification of 3D biopolymer networks. Sci. Rep. 5,
9081 (2015)
88. M. Jiang, Q. Ji, B.F. McEwen, Model-based automated extraction of microtubules from
electron tomography volume. IEEE Trans. Inf. Technol. Biomed. 10, 608–617 (2006)
89. D. Nurgaliev, T. Gatanov, D.J. Needleman, Automated identification of microtubules in
cellular electron tomography. Methods Cell Biol. 97, 475–495 (2010)
90. K. Sandberg, Methods for image segmentation in cellular tomography. Methods Cell Biol.
79, 769–798 (2007)
316
N. Volkmann
J. Struct. Biol. 192, 307–311 (2015)
69. Z. Yu, A.S. Frangakis, M-free: scoring the reference bias in sub-tomogram averaging and
template matching. J. Struct. Biol. 187, 10–19 (2014)
70. X.P. Xu, C. Page, N. Volkmann, Efficient extraction of macromolecular complexes from
electron tomograms based on reduced representation templates. Lect. Notes Comput. Sci.
9256, 423–431 (2015)
71. N. Volkmann, An approach to automated particle picking from electron micrographs based
on reduced representation templates. J. Struct. Biol. 145, 152–156 (2004)
72. J. Pierson, M. Vos, J.R. McIntosh, P.J. Peters, Perspectives on electron cryo-tomography of
vitreous cryo-sections. J. Electron. Microsc. (Tokyo) 60(Suppl 1), S93–S100 (2011)
73. A. Buades, B. Coll, J.-M. Morel, A non-local algorithm for image denoising. IEEE Comput.
Soc. Conf. Comp. Vis. Pattern Recog. 2, 60–65 (2005)
74. C.M. Spahn, E. Jan, A. Mulder, R.A. Grassucci, P. Sarnow, J. Frank, Cryo-EM visualization
of a viral internal ribosome entry site bound to human ribosomes: the IRES functions as an
RNA-based translation factor. Cell 118, 465–475 (2004)
75. J. Wu, B. Rajwa, D.L. Filmer, C.M. Hoffmann, B. Yuan, C. Chiang, J. Sturgis,
J.P. Robinson, Automated quantification and reconstruction of collagen matrix from 3D
confocal datasets. J. Microsc. 210, 158–165 (2003)
76. A.M. Stein, D.A. Vader, L.M. Jawerth, D.A. Weitz, L.M. Sander, An algorithm for
extracting the network geometry of three-dimensional collagen gels. J. Microsc. 232, 463–
475 (2008)
77. G. Herberich, R. Windoffer, R. Leube, T. Aach, 3D segmentation of keratin intermediate
filaments in confocal laser scanning microscopy, in Proceedings of Annual International
Conference of the IEEE EMBS (2011) pp. 7751–7754
78. S. Basu, C. Liu, G.K. Rohde, Localizing and extracting filament distributions from
microscopy images. J. Microsc. 258, 13–23 (2015)
79. M. Alioscha-Perez, C. Benadiba, K. Goossens, S. Kasas, G. Dietler, R. Willaert, H. Sahli,
A robust actin filaments image analysis framework. PLoS Comput. Biol. 12, e1005063 (2016)
80. W. Mickel, S. Münster, L.M. Jawerth, D.A. Vader, D.A. Weitz, A.P. Sheppard, K. Mecke,
B. Fabry, G.E. Schröder-Turk, Robust pore size analysis of filamentous networks from
three-dimensional confocal microscopy. Biophys. J. 95, 6072–6080 (2008)
81. B. Eltzner, C. Wollnik, C. Gottschlich, S. Huckemann, F. Rehfeldt, The filament sensor for
near real-time detection of cytoskeletal fiber structures. PLoS ONE 10, e0126346 (2015)
82. L.A. Loss, G. Bebis, B. Parvin, Iterative tensor voting for perceptual grouping of ill-defined
curvilinear structures. IEEE Trans. Med. Imaging 30, 1503–1513 (2011)
83. M. Beil, H. Braxmeier, F. Fleischer, V. Schmidt, P. Walther, Quantitative analysis of keratin
filament networks in scanning electron microscopy images of cancer cells. J. Microsc. 220,
84–95 (2005)
84. S. Lück, M. Sailer, V. Schmidt, P. Walther, Three-dimensional analysis of intermediate
filament networks using SEM tomography. J. Microsc. 239, 1–16 (2010)
85. D. Mayerich, J. Keyser, Hardware accelerated segmentation of complex volumetric filament
networks. IEEE Trans. Vis. Comput. Graph. 15, 670–681 (2009)
86. T. Xu, D. Vavylonis, X. Huang, 3D actin network centerline extraction with multiple active
contours. Med. Image Anal. 18, 272–284 (2014)
87. T. Xu, D. Vavylonis, F.C. Tsai, G.H. Koenderink, W. Nie, E. Yusuf, I-Ju Lee, J.Q. Wu,
X. Huang, SOAX: a software for quantification of 3D biopolymer networks. Sci. Rep. 5,
9081 (2015)
88. M. Jiang, Q. Ji, B.F. McEwen, Model-based automated extraction of microtubules from
electron tomography volume. IEEE Trans. Inf. Technol. Biomed. 10, 608–617 (2006)
89. D. Nurgaliev, T. Gatanov, D.J. Needleman, Automated identification of microtubules in
cellular electron tomography. Methods Cell Biol. 97, 475–495 (2010)
90. K. Sandberg, Methods for image segmentation in cellular tomography. Methods Cell Biol.
79, 769–798 (2007)
316
N. Volkmann