Interactive visual exploration of overlapping similar structures for three-dimensional microscope images

BMC Bioinformatics

Megumi Nakao³  Shintaro Takemoto³  Tadao Sugiura⁴  Kazuaki Sawada¹  Ryosuke Kawakami²  Tomomi Nemoto²  Tetsuya Matsuda³ 

[1] Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Hokkaido, Japan

[2] Research Institute for Electronic Science, Hokkaido University, Sapporo, Japan

[3] Graduate School of Informatics, Kyoto University, Yoshida Honmachi, Sakyo, Kyoto, Japan

[4] Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5, Takayama, Ikoma, Nara, Japan

关键词: Microscopic images;    Neural structures;    Multi-dimensional transfer functions;    Interactive visualization;   

Others  :  1084280
DOI  :  10.1186/s12859-014-0415-x

 received in 2014-07-30, accepted in 2014-12-09,  发布年份 2014

【 摘 要 】

Background

Recent advances in microscopy enable the acquisition of large numbers of tomographic images from living tissues. Three-dimensional microscope images are often displayed with volume rendering by adjusting the transfer functions. However, because the emissions from fluorescent materials and the optical properties based on point spread functions affect the imaging results, the intensity value can differ locally, even in the same structure. Further, images obtained from brain tissues contain a variety of neural structures such as dendrites and axons with complex crossings and overlapping linear structures. In these cases, the transfer functions previously used fail to optimize image generation, making it difficult to explore the connectivity of these tissues.

Results

This paper proposes an interactive visual exploration method by which the transfer functions are modified locally and interactively based on multidimensional features in the images. A direct editing interface is also provided to specify both the target region and structures with characteristic features, where all manual operations can be performed on the rendered image. This method is demonstrated using two-photon microscope images acquired from living mice, and is shown to be an effective method for interactive visual exploration of overlapping similar structures.

Conclusions

An interactive visualization method was introduced for local improvement of visualization by volume rendering in two-photon microscope images containing regions in which linear nerve structures crisscross in a complex manner. The proposed method is characterized by the localized multidimensional transfer function and interface where the parameters can be determined by the user to suit their particular visualization requirements.

【 授权许可】

   
2014 Nakao et al.; licensee BioMed Central.

【 图 表 】

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【 参考文献 】

• [1]Matsuzaki M, Honkura N, Ellis-Davies GC, Kasai H: Structural basis of long-term potentiation in single dendritic spines. Nature 2004, 429:761-766.
• [2]Nicholson DA, Trana R, Katz Y, Kath WL, Spruston N, Geinisman Y: Distance-dependent differences in synapse number and AMPA receptor expression in hippocampal CA1 pyramidal neurons. Neuron 2006, 50:431-442.
• [3]Nakashiba T, Cushman JD, Pelkey KA, Renaudineau S, Buhl DL, McHugh TJ, Rodriguez Barrera V, Chittajallu R, Iwamoto KS, McBain CJ, Fanselow MS, Tonegawa S: Young dentate granule cells mediate pattern separation, whereas old granule cells facilitate pattern completion. Cell 2012, 149:188-201.
• [4]Kawakami R, Sawada K, Sato A, Hibi T, Kozawa Y, Sato S Yokoyama H, Nemoto T: Visualizing hippocampal neurons with in vivo two-photon microscopy using a 1030 Nm picosecond pulse laser.Sci Rep 2013, 3.
• [5]Pan F, Gan WB: Two-photon imaging of dendritic spine development in the mouse cortex. Dev Neurobiology 2008, 68:771-778.
• [6]Trachtenberg JT, Chen BE, Knott GW, Feng G, Sanes JR, Welker E, Svoboda K: Long-term in vivo imaging of experience-dependent synaptic plasticity in adult cortex. Nature 2002, 420:788-794.
• [7]Peng H, Ruan Z, Long F, Simpson JH, Myers EW: V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets. Nature Biotec 2010, 28(4):348-353.
• [8]Peng H, Tang J, Xiao H, Bria A, Zhou J, Butler V, Zhou Z, Gonzalez-Bellido PT, Oh SW, Chen J, Mitra A, Tsien RW, Zeng H, Ascoli GA, Iannello G, Hawrylycz M, Myers E, Long F: Virtual finger boosts three-dimensional imaging and microsurgery as well as terabyte volume image visualization and analysis.Nat Commun 2014, 5. http://dx.doi.org/10.1038/ncomms5342
• [9]Wan Y, Otsuna H, Chien C, Hansen C: An interactive visualization tool for multi-channel confocal microscopy data in neurobiology research. IEEE Trans Visual Compute Graphics 2009, 15(6):1489-1496.
• [10]Heinmann T, Meinzer HP: Statistical shape models for 3D medical image segmentation: a review. Med Image Analysis 2009, 13(4):543-563.
• [11]Guerrero R, Wolz R, Rueckert D: Laplacian eigenmaps manifold learning for landmark localization in brain MR images.Med Imaging Comput Comput-Assist Interven 2011, 566-573
• [12]Levoy M: Efficient ray-tracing of volume data. ACM Trans Graphics 1990, 9(3):256-261.
• [13]Cabral B, Cam N, Foran J: Accelerated volume rendering and tomographic reconstruction using texture mapping hardware. Proc. of Volume Visualization Symposium 1994, 91-98
• [14]Lum E, Ma K: Lighting transfer functions using gradient aligned sampling. IEEE Visualization 2004, 289-296
• [15]Kniss J, Kindleman G, Hanse C: Multidimensional transfer functions for interactive volume rendering. IEEE Trans Visual Comp Graphics 2002, 8(3):270-285.
• [16]Kindlemann G, Whitaker R, Tasdizen T, Moller T, Curvature-based transfer-function for direct volume rendering methods and applications.IEEE Visualization 2003, 513-520
• [17]Frani AF, Nissen WJ, Vicken KL, Vergerver MA: Multiscale vessel enhancement filtering. Med Imaging Compute Computer Assisted Intervention 1998, 1496:130-137.
• [18]Sato Y, Westin C, Bhalerao A, Nakajima S, Shiraga N, Tamura S, Kikins R: Tissue classification based on 3d local intensity structures for volume rendering. IEEE Trans Visual Comp Graph 2000, 6(2):160-180.
• [19]Shikata H, Kitaoka H, Sato Y, Johkou T: Quantitative evaluation of spatial distribution of line structure in the lung for computer-aided diagnosis of pulmonary nodules. Systems Comp Japan 2003, 34(9):58-70.
• [20]Praßni J, Ropinski T, Mensmann J, Hinrichs K: Shape-based transfer functions for volume visualization.IEEE Pacific Visualization 2010, 9-16
• [21]Caban J, Rheingans P: Texture-based transfer functions for direct volume rendering. IEEE Visual Comp Graph 2008, 14(6):1364-1371.
• [22]Sereda P, Bartrolı A, Serlie I, Gerritsen F: Visualization of boundaries in volumetric data sets using LH histograms. IEEE Visual Comp Graph 2006, 12(2):208-218.
• [23]Haidacher M, Patel D, Bruckner S, Kanitser A, Groller E: Volume visualization based on statistical transfer-function spaces. Proc. IEEE Pacific Visualization 2010, 17-24
• [24]Correa CD, Ma KL: Size-based transfer functions: a new volume exploration technique. IEEE Trans Visuali Comp Graph 2008, 14(6):1387.
• [25]Wu Y, Qu H: Interactive transfer function design based on editing direct volume rendered images. IEEE Trans Visual Comp Graph 2007, 13(5):1027-1040.
• [26]Zhou J, Takatsuka M: Automatic transfer function generation using contour tree controlled residue flow model and color harmonics. IEEE Trans Visual Comp Graph 2009, 15(6):1481-1488.
• [27]Läthén G, Lindholm S, Lenz R, Borga M: Automatic tuning of spatially varying transfer functions for blood vessel visualization. IEEE Trans Visual Comp Graph 2012, 18(12):2345-2354.
• [28]Julia N, Maurer M, Mueller K: A high-dimensional feature clustering approach to support knowledge-assisted visualization. Comp Graph 2009, 33(5):607-715.
• [29]Maciejewski R, Woo I, Chen W, Ebert D: Structuring feature space: a non-parametric method for volumetric transfer function generation. IEEE Trans Visual Comp Graph 2009, 15(6):1473-1480.
• [30]Chen M, Ebert D, Hagen H, Laramee RS, van Liere R, Ma K-L, Ribarsky W, Scheuermann G, Silver D: Data, information, and knowledge in visualization. IEEE Comp Graph and Anim 2009, 29(1):12-19.
• [31]Kubisch C, Glaer S, Neugebauer M, Preim B, Vessel visualization with volume rendering, Visualization in Medicine and Life Sciences II (Springer).Mathematics Visualiz 2012: 109-134
• [32]Imanishi K, Nakao M, Kioka M, Mori M, Yoshida M, Takahashi T, Minato K: Interactive bone drilling using a 2D pointing device to support microendoscopic discectomy planning, Int. J Comp Ass Radiology Surgery 2010, 5(5):461-469.
• [33]Nakao M, Kurebayashi K, Sugiura T, Sato T, Sawada K, Kawakami R, Nemoto T, Minato K, Matsuda T: Visualizing in vivo brain neural structures using volume rendered feature spaces. Comp in Biol Med 2014, 53:85-93.