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Immersive Analytics Applications in Life and Health Sciences

  • Tobias Czauderna
  • Jason Haga
  • Jinman Kim
  • Matthias Klapperstück
  • Karsten Klein
  • Torsten Kuhlen
  • Steffen Oeltze-Jafra
  • Björn Sommer
  • Falk Schreiber
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11190)

Abstract

Life and health sciences are key application areas for immersive analytics. This spans a broad range including medicine (e.g., investigations in tumour boards), pharmacology (e.g., research of adverse drug reactions), biology (e.g., immersive virtual cells) and ecology (e.g., analytics of animal behaviour). We present a brief overview of general applications of immersive analytics in the life and health sciences, and present a number of applications in detail, such as immersive analytics in structural biology, in medical image analytics, in neurosciences, in epidemiology, in biological network analysis and for virtual cells.

Keywords

Immersive analytics Applications Life sciences Health sciences 

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References

  1. 1.
    Akkiraju, N., Edelsbrunner, H., Fu, P., Qian, J.: Viewing geometric protein structures from inside a CAVE. IEEE Comput. Graph. Appl. 16(4), 58–61 (1996)CrossRefGoogle Scholar
  2. 2.
    Allen, N., Sudlow, C., Downey, P., Peakman, T., Danesh, J., Elliott, P., Gallacher, J., Green, J., Matthews, P., Pell, J., Sprosen, T., Collins, R.: UK Biobank: current status and what it means for epidemiology. Health Policy Technol. 1(3), 123–126 (2012)CrossRefGoogle Scholar
  3. 3.
    Anderson, A., Weng, Z.: VRDD: applying virtual reality visualization to protein docking and design. J. Mol. Graph. Model. 17, 180–186 (1999)CrossRefGoogle Scholar
  4. 4.
    Angelelli, P., Oeltze, S., Turkay, C., Haász, J., Hodneland, E., Lundervold, A., Lundervold, A.J., Preim, B., Hauser, H.: Interactive visual analysis of heterogeneous cohort study data. IEEE Comput. Graph. Appl. 34(5), 70–82 (2014)CrossRefGoogle Scholar
  5. 5.
    Asai, K., Takase, N.: Learning molecular structures in a tangible augmented reality environment. Int. J. Virtual Pers. Learn. Environ. 2(1), 1–18 (2011)CrossRefGoogle Scholar
  6. 6.
    Avogadro. http://avogadro.cc/. Accessed 20 Apr 2017
  7. 7.
    Bernard, J., Sessler, D., May, T., Schlomm, T., Pehrke, D., Kohlhammer, J.: A visual-interactive system for prostate cancer cohort analysis. IEEE Comput. Graph. Appl. 35(3), 44–55 (2015)CrossRefGoogle Scholar
  8. 8.
    Bi, L., Kim, J., Kumar, A., Wen, L., Feng, D., Fulham, M.: Automatic detection and classification of regions of FDG uptake in whole-body PET-CT lymphoma studies. Comput. Med. Imaging Graph. 60, 3–10 (2017)CrossRefGoogle Scholar
  9. 9.
    Biehl, J.T., Baker, W.T., Bailey, B.P., Tan, D.S., Inkpen, K.M., Czerwinski, M.: IMPROMPTU: a new interaction framework for supporting collaboration in multiple display environments and its field evaluation for co-located software development. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2008, pp. 939–948. ACM, New York (2008)Google Scholar
  10. 10.
    Binder, J.X., et al.: COMPARTMENTS: unification and visualization of protein subcellular localization evidence. Database (2014).  https://doi.org/10.1093/database/bau012CrossRefGoogle Scholar
  11. 11.
    Brown, J., Wilson, J., Gossage, S., Hack, C., Biddle, R.: Surface computing and collaborative analysis work. In: Synthesis Lectures on Human-Centered Informatics. Morgan & Claypool Publishers (2013)Google Scholar
  12. 12.
    Campbell, I.D.: The march of structural biology. Nat. Rev. Mol. Cell Biol. 3(5), 377–381 (2002)CrossRefGoogle Scholar
  13. 13.
    Carnevale, N.T., Hines, M.L.: The NEURON Book. Cambridge University Press, New York (2006)CrossRefGoogle Scholar
  14. 14.
    Chandler, T., et al.: Immersive analytics. In: IEEE Big Data Visual Analytics (BDVA 2015), pp. 73–80. IEEE eXpress Conference Publishing (2015)Google Scholar
  15. 15.
    Chen, M., Hofestädt, R.: Approaches in Integrative Bioinformatics: Towards the Virtual Cell. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-642-41281-3CrossRefGoogle Scholar
  16. 16.
    Chimera. http://www.cgl.ucsf.edu/chimera/. Accessed 20 Apr 2017
  17. 17.
    Coles, T.R., John, N.W., Gould, D., Caldwell, D.G.: Integrating haptics with augmented reality in a femoral palpation and needle insertion training simulation. IEEE Trans. Haptics 4(3), 199–209 (2011)CrossRefGoogle Scholar
  18. 18.
    Cordeil, M., Dwyer, T., Klein, K., Laha, B., Marriot, K., Thomas, B.H.: Immersive collaborative analysis of network connectivity: CAVE-style or head-mounted display? IEEE Trans. Vis. Comput. Graph. 23, 441–450 (2017)CrossRefGoogle Scholar
  19. 19.
    Cosentino, F., John, N.W., Vaarkamp, J.: An overview of augmented and virtual reality applications in radiotherapy and future developments enabled by modern tablet devices. J. Radiother. Pract. 13(3), 350–364 (2014)CrossRefGoogle Scholar
  20. 20.
    Crivelli, S., Kreylos, O., Hamann, B., Max, N., Bethel, W.: ProteinShop: a tool for interactive protein manipulation and steering. J. Comput. Aided Mol. Des. 18(4), 271–285 (2004)CrossRefGoogle Scholar
  21. 21.
    Cruz-Neira, C., et al.: Scientists in wonderland: a report on visualization applications in the CAVE virtual reality environment. In: Proceedings of 1993 IEEE Research Properties in Virtual Reality Symposium, pp. 59–66 (1993)Google Scholar
  22. 22.
    Cruz-Neira, C., Sandin, D.J., DeFanti, T.A.: Surround-screen projection-based virtual reality: the design and implementation of the CAVE. In: Proceedings of the 20th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH 1993), pp. 135–142. ACM (1993)Google Scholar
  23. 23.
    Cruz-Neira, C., Sandin, D.J., DeFanti, T.A., Kenyon, R.V., Hart, J.C.: The CAVE: audio visual experience automatic virtual environment. Commun. ACM 35(6), 64–72 (1992)CrossRefGoogle Scholar
  24. 24.
    Czauderna, T., Klukas, C., Schreiber, F.: Editing, validating and translating of SBGN maps. Bioinformatics 26(18), 2340–2341 (2010)CrossRefGoogle Scholar
  25. 25.
    Czauderna, T., Wybrow, M., Marriott, K., Schreiber, F.: Conversion of KEGG metabolic pathways to SBGN maps including automatic layout. BMC Bioinform. 14, 250 (2013)CrossRefGoogle Scholar
  26. 26.
    Czernuszenko, M., Pape, D., Sandin, D., DeFanti, T., Dawe, G.L., Brown, M.D.: The ImmersaDesk and infinity wall projection-based virtual reality displays. ACM SIGGRAPH Comput. Graph. 31(2), 46–49 (1997)CrossRefGoogle Scholar
  27. 27.
    de Ridder, M., Jung, Y., Huang, R., Kim, J., Feng, D.D.: Exploration of virtual and augmented reality for visual analytics and 3D volume rendering of functional magnetic resonance imaging (fMRI) data. In: IEEE Big Data Visual Analytics (BDVA 2015), pp. 49–56. IEEE eXpress Conference Publishing (2015)Google Scholar
  28. 28.
    de Ridder, M., Klein, K., Kim, J.: CereVA - visual analysis of functional brain connectivity. In: Proceedings of the 6th International Conference on Information Visualization Theory and Applications (IVAPP 2015), pp. 131–138. SciTePress (2015)Google Scholar
  29. 29.
    Dreher, M., et al.: Interactive molecular dynamics: scaling up to large systems. Procedia Comput. Sci. 18, 20–29 (2013)CrossRefGoogle Scholar
  30. 30.
    Dwyer, T., Marriott, K., Schreiber, F., Stuckey, P.J., Woodward, M., Wybrow, M.: Exploration of networks using overview+detail with constraint-based cooperative layout. IEEE Trans. Vis. Comput. Graph. 14(6), 1293–1300 (2008)CrossRefGoogle Scholar
  31. 31.
    Ellis, S.E., Groth, D.P.: A collaborative annotation system for data visualization. In: Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2004, pp. 411–414, ACM, New York (2004)Google Scholar
  32. 32.
    Emerson, J.W., et al.: The generalized pairs plot. J. Comput. Graph. Stat. 22(1), 79–91 (2013)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Everitt, K., Shen, C., Ryall, K., Forlines, C.: MultiSpace: enabling electronic document micro-mobility in table-centric, multi-device environments. In: Proceedings of the First IEEE International Workshop on Horizontal Interactive Human-Computer Systems, TABLETOP 2006, pp. 27–34. IEEE Computer Society, Washington, DC (2006)Google Scholar
  34. 34.
    Falk, M., Krone, M., Ertl, T.: Atomistic visualization of mesoscopic whole-cell simulations using ray-casted instancing. In: Computer Graphics Forum, vol. 32, pp. 195–206. Wiley Online Library (2013)Google Scholar
  35. 35.
    Febretti, A., Nishimoto, A., Mateevitsi, V., Renambot, L., Johnson, A., Leigh, J.: Omegalib: a multi-view application framework for hybrid reality display environments. In: IEEE Virtual Reality, pp. 9–14 (2014)Google Scholar
  36. 36.
    Febretti, A., et al.: CAVE2: a hybrid reality environment for immersive simulation and information analysis. In: IS&T/SPIE Electronic Imaging, vol. 8649, pp. 864903.1–864903.12. International Society for Optics and Photonics (2013)Google Scholar
  37. 37.
    German National Cohort Consortium: The German National Cohort: aims, study design and organization. Eur. J. Epidemiol. 29(5), 371–382 (2014)CrossRefGoogle Scholar
  38. 38.
    Geurts, A., Sakas, G., Kuijper, A., Becker, M., Landesberger, T.: Visual comparison of 3d medical image segmentation algorithms based on statistical shape models. In: Duffy, V.G. (ed.) DHM 2015. LNCS, vol. 9185, pp. 336–344. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-21070-4_34CrossRefGoogle Scholar
  39. 39.
    Gewaltig, M.O., Diesmann, M.: NEST (neural simulation tool). Scholarpedia 2(4), 1430 (2007)CrossRefGoogle Scholar
  40. 40.
    Gillet, A., Sanner, M., Stoffler, D., Goodsell, D., Olson, A.: Augmented reality with tangible auto-fabricated models for molecular biology applications. In: Proceedings of the Conference on Visualization 2004 (VIS 2004), pp. 235–242. IEEE Computer Society (2004)Google Scholar
  41. 41.
    Gillet, A., Sanner, M., Stoffler, D., Olson, A.: Tangible interfaces for structural molecular biology. Structure 13(3), 483–491 (2005)CrossRefGoogle Scholar
  42. 42.
    Giraldo-Chica, M., Woodward, N.D.: Review of thalamocortical resting-state fMRI studies in schizophrenia. Schizophr. Res. 180, 58–63 (2017)CrossRefGoogle Scholar
  43. 43.
    Glaßer, S., Preim, U., Tönnies, K., Preim, B.: A visual analytics approach to diagnosis of breast DCE-MRI data. Comput. Graph. 34(5), 602–611 (2010)CrossRefGoogle Scholar
  44. 44.
    GLmol. http://webglmol.osdn.jp/index-en.html. Accessed 20 Apr 2017
  45. 45.
    Grafahrend-Belau, E., Weise, S., Koschützki, D., Scholz, U., Junker, B.H., Schreiber, F.: MetaCrop - a detailed database of crop plant metabolism. Nucleic Acids Res. 36, D954–D958 (2008)CrossRefGoogle Scholar
  46. 46.
    Greffard, N., Picarougne, F., Kuntz, P.: Visual community detection: an evaluation of 2D, 3D perspective and 3D stereoscopic displays. In: van Kreveld, M., Speckmann, B. (eds.) GD 2011. LNCS, vol. 7034, pp. 215–225. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-25878-7_21CrossRefGoogle Scholar
  47. 47.
    Hartigan, J.A., Kleiner, B.: Mosaics for contingency tables. In: Eddy, W.F. (ed.) Computer Science and Statistics: Proceedings of the 13th Symposium on the Interface. Springer, New York (1981).  https://doi.org/10.1007/978-1-4613-9464-8_37CrossRefGoogle Scholar
  48. 48.
    Heer, J., Agrawala, M.: Design considerations for collaborative visual analytics. Inf. Vis. 7(1), 49–62 (2008)CrossRefGoogle Scholar
  49. 49.
    Hegenscheid, K., Kühn, J., Völzke, H., Biffar, R., Hosten, N., Puls, R.: Whole-body magnetic resonance imaging of healthy volunteers: pilot study results from the population-based SHIP study. RoFo: Fortschritte auf dem Gebiete der Röntgenstrahlen und der. Nuklearmedizin 181(8), 748–759 (2009)Google Scholar
  50. 50.
    Hermann, M., Klein, R.: A visual analytics perspective on shape analysis: state of the art and future prospects. Comput. Graph. 53, Part A, 63–71 (2015)CrossRefGoogle Scholar
  51. 51.
    Hofman, A., et al.: The Rotterdam study: 2016 objectives and design update. Eur. J. Epidemiol. 30(8), 661–708 (2015)CrossRefGoogle Scholar
  52. 52.
    Hucka, M., Finney, A., Sauro, H.M., Bolouri, H., Doyle, J.C., Kitano, H., Arkin, A.P., et al.: The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19, 524–531 (2003)CrossRefGoogle Scholar
  53. 53.
    The Human Brain Project. https://www.humanbrainproject.eu/. Accessed 20 Apr 2017
  54. 54.
    Im, W., Liang, J., Olson, A., Zhou, H.X., Vajda, S., Vakser, I.A.: Challenges in structural approaches to cell modeling. J. Mol. Biol. 428(15), 2943–2964 (2016)CrossRefGoogle Scholar
  55. 55.
    Inselberg, A., Dimsdale, B.: Parallel coordinates: a tool for visualizing multi-dimensional geometry. In: Proceedings of IEEE Visualization, pp. 361–378 (1990)Google Scholar
  56. 56.
    Isenberg, P., Elmqvist, N., Scholtz, J., Cernea, D., Ma, K.L., Hagen, H.: Collaborative visualization: definition, challenges, and research agenda. Inf. Vis. 10(4), 310–326 (2011)CrossRefGoogle Scholar
  57. 57.
    Jmol. http://jmol.sourceforge.net/. Accessed 20 Apr 2017
  58. 58.
    Johanson, B., Fox, A., Winograd, T.: The interactive workspaces project: experiences with ubiquitous computing rooms. IEEE Pervasive Comput. 1(2), 67–74 (2002)CrossRefGoogle Scholar
  59. 59.
    Johnson, G.T., Autin, L., Goodsell, D.S., Sanner, M.F., Olson, A.J.: ePMV embeds molecular modeling into professional animation software environments. Structure 19(3), 293–303 (2011)CrossRefGoogle Scholar
  60. 60.
    Johnson, G.T., Autin, L., Al-Alusi, M., Goodsell, D.S., Sanner, M.F., Olson, A.J.: cellPACK: a virtual mesoscope to model and visualize structural systems biology. Nature Methods 12(1), 85–91 (2015)CrossRefGoogle Scholar
  61. 61.
    Johnson, G.R., Donovan-Maiye, R.M., Maleckar, M.M.: Generative modeling with conditional autoencoders: building an integrated cell. arXiv preprint arXiv:1705.00092 (2017)
  62. 62.
    Junker, B.H., Klukas, C., Schreiber, F.: VANTED: a system for advanced data analysis and visualization in the context of biological networks. BMC Bioinform. 7(1), 109.1–109.13 (2006)Google Scholar
  63. 63.
    Kahin, B., Keller, J.H.: The self-governing internet: coordination by design. In: Kahin, B., Keller, J.H. (eds.) Coordinating the Internet, pp. 3–38. MIT Press, Cambridge (1997)Google Scholar
  64. 64.
    Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., Tanabe, M.: KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40(1), D109–D114 (2012)CrossRefGoogle Scholar
  65. 65.
    Kerren, A., Schreiber, F.: Toward the role of interaction in visual analytics. In: Proceedings of the Winter Simulation Conference, vol. 420, pp. 1–13 (2012)Google Scholar
  66. 66.
    Kerren, A., Schreiber, F.: Network visualization for integrative bioinformatics. In: Chen, M., Hofestädt, R. (eds.) Approaches in Integrative Bioinformatics, pp. 173–202. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-642-41281-3_7CrossRefGoogle Scholar
  67. 67.
    Kieffer, S., Dwyer, T., Marriott, K., Wybrow, M.: HOLA: human-like orthogonal network layout. IEEE Trans. Vis. Comput. Graph. 22(1), 349–358 (2016)CrossRefGoogle Scholar
  68. 68.
    Klapperstück, M., Czauderna, T., Goncu, C., Glowacki, J., Dwyer, T., Schreiber, F., Marriott, K.: ContextuWall: peer collaboration using (large) displays. IEEE Big Data Vis. Anal. (BDVA) 2016, 7–14 (2016)Google Scholar
  69. 69.
    Klemm, P., Glaßer, S., Lawonn, K., Rak, M., Völzke, H., Hegenscheid, K., Preim, B.: Interactive visual analysis of lumbar back pain. In: International Conference on Information Visualization Theory and Applications (IVAPP), pp. 85–92 (2015)Google Scholar
  70. 70.
    Klemm, P., et al.: Visualization and analysis of lumbar spine canal variability in cohort study data. In: Bronstein, M., Favre, J., Hormann, K. (eds.) International Workshop on Vision, Modeling and Visualization (VMV), pp. 121–128 (2013)Google Scholar
  71. 71.
    Klemm, P., Oeltze-Jafra, S., Lawonn, K., Hegenscheid, K., Völzke, H., Preim, B.: Interactive visual analysis of image-centric cohort study data. IEEE Trans. Vis. Comput. Graph. 20(12), 1673–1682 (2014)CrossRefGoogle Scholar
  72. 72.
    Klukas, C., Schreiber, F.: Dynamic exploration and editing of KEGG pathway diagrams. Bioinformatics 23(3), 344–350 (2007)CrossRefGoogle Scholar
  73. 73.
    Krause, J., Perer, A., Stavropoulos, H.: Supporting iterative cohort construction with visual temporal queries. IEEE Trans. Vis. Comput. Graph. 22(1), 91–100 (2016)CrossRefGoogle Scholar
  74. 74.
    Kuhlen, T.W., Hentschel, B.: Towards an explorative visual analysis of cortical neuronal network simulations. In: Grandinetti, L., Lippert, T., Petkov, N. (eds.) BrainComp 2013. LNCS, vol. 8603, pp. 171–183. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-12084-3_14CrossRefGoogle Scholar
  75. 75.
    Kuhlen, T.W., Hentschel, B.: Towards the ultimate display for neuroscientific data analysis. In: Amunts, K., Grandinetti, L., Lippert, T., Petkov, N. (eds.) BrainComp 2015. LNCS, vol. 10087, pp. 157–168. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-50862-7_12CrossRefGoogle Scholar
  76. 76.
    Kumar, A., Kim, J., Lyndon, D., Fulham, M., Feng, D.: An ensemble of fine-tuned convolutional neural networks for medical image classification. IEEE J. Biomed. Health Inform. 21(1), 31–40 (2017)CrossRefGoogle Scholar
  77. 77.
    Kumar, A., Nette, F., Klein, K., Fulham, M., Kim, J.: A visual analytics approach using the exploration of multidimensional feature spaces for content-based medical image retrieval. IEEE J. Biomed. Health Inform. 19(5), 1734–1746 (2015)CrossRefGoogle Scholar
  78. 78.
    Lander, A.D.: The edges of understanding. BMC Biol. 8(1), 40.1–40.4 (2010)CrossRefGoogle Scholar
  79. 79.
    Langs, G., Hanbury, A., Menze, B., Müller, H.: VISCERAL: towards large data in medical imaging — challenges and directions. In: Greenspan, H., Müller, H., Syeda-Mahmood, T. (eds.) MCBR-CDS 2012. LNCS, vol. 7723, pp. 92–98. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-36678-9_9CrossRefGoogle Scholar
  80. 80.
    Lau, C.D., Levesque, M.J., Chien, S., Date, S., Haga, J.H.: ViewDock TDW: high-throughput visualization of virtual screening results. Bioinformatics 26(15), 1915–1917 (2010)CrossRefGoogle Scholar
  81. 81.
    Le Novère, N.: The systems biology graphical notation. Nat. Biotechnol. 27, 735–741 (2009)CrossRefGoogle Scholar
  82. 82.
    Levinthal, C., Barry, C.D., Ward, S.A., Zwick, M.: Computer graphics in macromolecular chemistry. In: Emerging Concepts in Computer Graphics, pp. 231–253. W. A. Benjamin (1968)Google Scholar
  83. 83.
    Liao, Z., et al.: A visual analytics approach for detecting and understanding anomalous resident behaviors in smart healthcare. Appl. Sci. 7(3), 254.1–254.13 (2017)Google Scholar
  84. 84.
    Loew, L.M., Schaff, J.C.: The Virtual Cell: a software environment for computational cell biology. Trends Biotechnol. 19(10), 401–406 (2001)CrossRefGoogle Scholar
  85. 85.
    Marai, G.E., Forbes, A.G., Johnson, A.: Interdisciplinary immersive analytics at the Electronic Visualization Laboratory: lessons learned and upcoming challenges. In: IEEE VR 2016 Workshop on Immersive Analytics, pp. 1–6 (2016)Google Scholar
  86. 86.
    Meng, E.C., Pettersen, E.F., Couch, G.S., Huang, C.C., Ferrin, T.E.: Tools for integrated sequence-structure analysis with UCSF Chimera. BMC Bioinform. 7(1), 339–348 (2006)CrossRefGoogle Scholar
  87. 87.
    Moore, P.B.: Structural biology: past, present, and future. New Biotechnol. (2016)Google Scholar
  88. 88.
    Moritz, E., Meyer, J.: Virtual exploration of proteins. In: Proceedings of the Second IASTED International Conference on Visualization, Imaging, and Image Processing (VIIP 2002), pp. 757–762 (2002)Google Scholar
  89. 89.
    Moritz, E., Meyer, J.: Interactive 3D protein structure visualization using virtual reality. In: Proceedings of the Fourth IEEE Symposium on Bioinformatics and Bioengineering (IEEE BIBE 2004), pp. 503–507 (2004)Google Scholar
  90. 90.
    Moritz, E., Wischgoll, T., Meyer, J.: Comparison of input devices and displays for protein visualization. ACM Crossroads 12(2), 19–26 (2005)CrossRefGoogle Scholar
  91. 91.
    Morris, J.H., Huang, C.C., Babbitt, P.C., Ferrin, T.E.: structureViz: linking Cytoscape and UCSF Chimera. Bioinformatics 23(17), 2345–2347 (2007)CrossRefGoogle Scholar
  92. 92.
    Morrison, J.: Will chemists tilt their heads for virtual reality? Chem. Eng. News 94(14), 22–23 (2016)Google Scholar
  93. 93.
    Mueller, J., Butscher, S., Reiterer, H.: Immersive analysis of health-related data with mixed reality interfaces: potentials and open questions. In: Workshop Immersive Analytics 2016 (in conjunction with ISS 2016) (2016)Google Scholar
  94. 94.
    Müller, H., Kalpathy–Cramer, J., Caputo, B., Syeda-Mahmood, T., Wang, F.: Overview of the first workshop on medical content–based retrieval for clinical decision support at MICCAI 2009. In: Caputo, B., Müller, H., Syeda-Mahmood, T., Duncan, J.S., Wang, F., Kalpathy-Cramer, J. (eds.) MCBR-CDS 2009. LNCS, vol. 5853, pp. 1–17. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-11769-5_1CrossRefGoogle Scholar
  95. 95.
    Nadan, T., Haffegee, A., Watson, K.: Collaborative and parallelized immersive molecular docking. In: Allen, G., Nabrzyski, J., Seidel, E., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2009. LNCS, vol. 5545, pp. 737–745. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-01973-9_82CrossRefGoogle Scholar
  96. 96.
    Ni, T., Schmidt, G.S., Staadt, O.G., Livingston, M.A., Ball, R., May, R.: A survey of large high-resolution display technologies, techniques, and applications. In: Proceedings of the IEEE Conference on Virtual Reality (VR 2006), pp. 223–236. IEEE Computer Society (2006)Google Scholar
  97. 97.
    Nim, H., Done, T., Schreiber, F., Boyd, S.: Interactive geolocational and coral compositional visualisation of great barrier reef heat stress data. IEEE Big Data Vis. Anal. (BDVA) 2015, 1–7 (2015)Google Scholar
  98. 98.
    Nim, H.T., Wang, M., Zhu, Y., Sommer, B., Schreiber, F., Boyd, S.E., Wang, S.J.: Communicating the effect of human behaviour on the Great Barrier Reef via mixed reality visualisation. IEEE Big Data Vis. Anal. (BDVA) 2016, 1–6 (2016)Google Scholar
  99. 99.
    Nim, H.T., et al.: Design considerations for immersive analytics of bird movements obtained by miniaturised GPS sensors. In: Bruckner, S., Hennemuth, A., Kainz, B., Hotz, I., Merhof, D., Rieder, C. (eds.) Eurographics Workshop on Visual Computing for Biology and Medicine. The Eurographics Association (2017)Google Scholar
  100. 100.
    Nowke, C., et al.: VisNEST - interactive analysis of neural activity data. In: IEEE Symposium on Biological Data Visualization (BioVis), pp. 65–72 (2013)Google Scholar
  101. 101.
    O’Donoghue, S.I., Sabir, K.S., Kalemanov, M., Stolte, C., Wellmann, B., Ho, V., Roos, M., Perdigão, N., Buske, F.A., Heinrich, J.: Aquaria: simplifying discovery and insight from protein structures. Nat. Methods 12(2), 98–99 (2015)CrossRefGoogle Scholar
  102. 102.
    Peters, M.V.: Cutting the “gordian knot” in early breast cancer. Ann. R. Coll.E Physicians Surg. Can. 8, 186–192 (1975)Google Scholar
  103. 103.
    Porta, M.S., Greenland, S., Hernán, M., dos Santos Silva, I., Last, J.M. (eds.): A Dictionary of Epidemiology, 6th edn. Oxford University Press, Oxford (2014)Google Scholar
  104. 104.
    Preim, B., et al.: Visual analytics of image-centric cohort studies in epidemiology. In: Linsen, L., Hamann, B., Hege, H.-C. (eds.) Visualization in Medicine and Life Sciences III. MV, pp. 221–248. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-24523-2_10CrossRefGoogle Scholar
  105. 105.
    PyMOL. https://www.pymol.org/. Accessed 20 Apr 2017
  106. 106.
    Raidou, R.G., et al.: Visual analytics for the exploration of tumor tissue characterization. Comput. Graph. Forum 34(3), 11–20 (2015)CrossRefGoogle Scholar
  107. 107.
    Rak, M., Engel, K., Tönnies, K.D.: Closed-form hierarchical finite element models for part-based object detection. In: International Workshop on Vision, Modeling and Visualization (VMV), pp. 137–144 (2013)Google Scholar
  108. 108.
    RasMol. http://www.openrasmol.org/. Accessed 20 Apr 2017
  109. 109.
    RCSB PDB: Molecular graphics software links. http://www.rcsb.org/pdb/static.do?p=software/software_links/molecular_graphics.html. Accessed 20 Apr 2017
  110. 110.
    Rekimoto, J., Saitoh, M.: Augmented surfaces: a spatially continuous work space for hybrid computing environments. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 1999, pp. 378–385. ACM, New York (1999)Google Scholar
  111. 111.
    Renambot, L., et al.: SAGE2: a collaboration portal for scalable resolution displays. Futur. Gener. Comput. Syst. 54, 296–305 (2016)CrossRefGoogle Scholar
  112. 112.
    Ribeiro, M.L., Lederman, H.M., Elias, S., Nunes, F.L.S.: Techniques and devices used in palpation simulation with haptic feedback. ACM Comput. Surv. 49(3), 48.1–48.28 (2016)CrossRefGoogle Scholar
  113. 113.
    Ritsos, P.D., John, N.W., Roberts, J.C.: Standards in augmented reality - towards prototyping haptic medical AR. In: 8th International AR Standards Meeting. Perey Research & Consulting, March 2013Google Scholar
  114. 114.
    Roberts, J.C., Ritsos, P.D., Badam, S.K., Brodbeck, D., Kennedy, J., Elmqvist, N.: Visualization beyond the desktop - the next big thing. IEEE Comput. Graph. Appl. 34(6), 26–34 (2014)CrossRefGoogle Scholar
  115. 115.
    Rohn, H., Klukas, C., Schreiber, F.: Creating views on integrated multidomain data. Bioinformatics 27(13), 1839–1845 (2011)CrossRefGoogle Scholar
  116. 116.
    Rohn, H., et al.: VANTED v2: a framework for systems biology applications. BMC Syst. Biol. 6(1), 139.1–139.13 (2012)Google Scholar
  117. 117.
    Sabir, K., Stolte, C., Tabor, B., O’Donoghue, S.: The molecular control toolkit: controlling 3D molecular graphics via gesture and voice. In: IEEE Symposium on Biological Data Visualization (BioVis), pp. 49–56. IEEE (2013)Google Scholar
  118. 118.
    Schmid, J., Kim, J., Magnenat-Thalmann, N.: Robust statistical shape models for MRI bone segmentation in presence of small field of view. Med. Image Anal. 15(1), 155–168 (2011)CrossRefGoogle Scholar
  119. 119.
    Schreiber, F., et al.: MetaCrop 2.0: managing and exploring information about crop plant metabolism. Nucleic Acids Res. 40, D1173–D1177 (2012)CrossRefGoogle Scholar
  120. 120.
    Schreiber, F., Dwyer, T., Marriott, K., Wybrow, M.: A generic algorithm for layout of biological networks. BMC Bioinform. 10, 375 (2009)CrossRefGoogle Scholar
  121. 121.
    Schultz, T., Kindlmann, G.L.: Open-box spectral clustering: applications to medical image analysis. IEEE Trans. Vis. Comput. Graph. 19(12), 2100–2108 (2013)CrossRefGoogle Scholar
  122. 122.
    Sera, C., Matlock, S., Watashiba, Y., Ichikawa, K., Haga, J.H.: Hydra: a high-throughput virtual screening data visualization and analysis tool. Procedia Comput. Sci. 80, 2312–2316 (2016)CrossRefGoogle Scholar
  123. 123.
    Silva, B.A.L., Renambot, L.: CytoViz: an artistic mapping of network measurements as living organisms in a VR application. In: Proceedings of SPIE, Stereoscopic Displays and Virtual Reality Systems XIV, vol. 6490, pp. 64901U.1–64901U.11. International Society for Optics and Photonics (2007)Google Scholar
  124. 124.
    Sommer, B., Barnes, D., Boyd, S., Chandler, T., Cordeil, M., Czauderna, T., Klapperstück, M., Klein, K., Nguyen, T.D., Nim, H., Stephens, K., Vohl, D., Wang, S., Wilson, E., Zhu, Y., Li, J., McCormack, J., Marriott, K., Schreiber, F.: 3D-Stereoscopic immersive analytics projects at Monash University and University of Konstanz. In: Proceedings IS&T Electronic Imaging - Stereoscopic Displays and Applications XXVIII, pp. 5.179–5.187 (2017)CrossRefGoogle Scholar
  125. 125.
    Sommer, B., et al.: Stereoscopic space map - semi-immersive configuration of 3D-stereoscopic tours in multi-display environments. In: Proceedings of IS&T Electronic Imaging - Stereoscopic Displays and Appl. XXVII, pp. 5.1–5.9 (2016)CrossRefGoogle Scholar
  126. 126.
    Sommer, B.: Subcellular localization charts: a new visual methodology for the semi-automatic localization of protein-related data sets. J. Bioinform. Comput. Biol. 11(1), 1340005.1–1340005.18 (2013)CrossRefGoogle Scholar
  127. 127.
    Sommer, B., Schreiber, F.: Integration and virtual reality exploration of biomedical data with CmPI and VANTED. Inf. Technol. 59(4), 181–190 (2017)Google Scholar
  128. 128.
    Sommer, B.: Membrane packing problems: a short review on computational membrane modeling methods and tools. Comput. Struct. Biotechnol. J. 5(6), e201302014.1–e201302014.13 (2013)CrossRefGoogle Scholar
  129. 129.
    Sommer, B., Bender, C., Hoppe, T., Gamroth, C., Jelonek, L.: Stereoscopic cell visualization: from mesoscopic to molecular scale. J. Electron. Imaging 23(1), 011007.1–011007.11 (2014)CrossRefGoogle Scholar
  130. 130.
    Sommer, B., Künsemöller, J., Sand, N., Husemann, A., Rumming, M., Kormeier, B.: CELLmicrocosmos 4.1: an interactive approach to integrating spatially localized metabolic networks into a virtual 3D cell environment. In: Fred, A., Filipe, J., Gamboa, H. (eds.) Proceedings of the International Conference on Bioinformatics (BIOINFORMATICS 2010), pp. 90–95 (2010)Google Scholar
  131. 131.
    Sommer, B., Wang, S.J., Xu, L., Chen, M., Schreiber, F.: Hybrid-dimensional visualization and interaction - integrating 2D and 3D visualization with semi-immersive navigation techniques. In: IEEE Big Data Visual Analytics (BDVA 2015), pp. 65–72. IEEE eXpress Conference Publishing (2015)Google Scholar
  132. 132.
    Stefik, M., Foster, G., Bobrow, D.G., Kahn, K., Lanning, S., Suchman, L.: Beyond the chalkboard: computer support for collaboration and problem solving in meetings. Commun. ACM 30(1), 32–47 (1987)CrossRefGoogle Scholar
  133. 133.
    Stoakley, R., Conway, M.J., Pausch, R.: Virtual reality on a WIM: interactive worlds in miniature. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 265–272. ACM Press/Addison-Wesley Publishing Co. (1995)Google Scholar
  134. 134.
    Stone, J.E., Sherman, W.R., Schulten, K.: Immersive molecular visualization with omnidirectional stereoscopic ray tracing and remote rendering. In: IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 1048–1057 (2016)Google Scholar
  135. 135.
    Streitz, N.A., et al.: i-LAND: an interactive landscape for creativity and innovation. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 1999, pp. 120–127. ACM, New York (1999)Google Scholar
  136. 136.
    Tönnies, K.D., et al.: Image analysis in epidemiological applications. IT - Inf. Technol. 57(1), 22–29 (2015)Google Scholar
  137. 137.
    Torsney-Weir, T., et al.: Tuner: principled parameter finding for image segmentation algorithms using visual response surface exploration. IEEE Trans. Vis. Comput. Graph. 17(12), 1892–1901 (2011)CrossRefGoogle Scholar
  138. 138.
    Turkay, C., Lundervold, A., Lundervold, A.J., Hauser, H.: Hypothesis generation by interactive visual exploration of heterogeneous medical data. In: Holzinger, A., Pasi, G. (eds.) HCI-KDD 2013. LNCS, vol. 7947, pp. 1–12. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-39146-0_1CrossRefGoogle Scholar
  139. 139.
    Unity Game Engine. https://unity3d.com/. Accessed 20 Apr 2017
  140. 140.
    van den Elzen, S., van Wijk, J.J.: Small multiples, large singles: a new approach for visual data exploration. Comput. Graph. Forum 32(3pt2), 191–200 (2013)CrossRefGoogle Scholar
  141. 141.
    Virtalis. https://www.virtalis.com/vr-for-pymol/. Accessed 20 Apr 2017
  142. 142.
    VMD. http://www.ks.uiuc.edu/Research/vmd/. Accessed 20 Apr 2017
  143. 143.
    VMD Required Libraries and Related Programs. http://www.ks.uiuc.edu/Research/vmd/allversions/related_programs.html. Accessed 20 Apr 2017
  144. 144.
    Völzke, H., et al.: Cohort profile: the study of health in Pomerania. Int. J. Epidemiol. 40(2), 294–307 (2011)Google Scholar
  145. 145.
    Weise, S., et al.: Meta-All: a system for managing metabolic pathway information. BMC Bioinform. 7, 465 (2006)CrossRefGoogle Scholar
  146. 146.
    Wheeler, A.: Understanding virtual reality headsets. http://www.engineering.com/Hardware/ArticleID/12699/. Accessed 20 Apr 2017
  147. 147.
    Widjaja, Y.Y., Pang, C.N.I., Li, S.S., Wilkins, M.R., Lambert, T.D.: The interactorium: visualising proteins, complexes and interaction networks in a virtual 3D cell. Proteomics 9(23), 5309–5315 (2009)CrossRefGoogle Scholar
  148. 148.
    Wigdor, D., Shen, C., Forlines, C., Balakrishnan, R.: Table-centric interactive spaces for real-time collaboration. In: Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2006, pp. 103–107. ACM, New York (2006)Google Scholar
  149. 149.
    Wilson, E.O., Ryan, M., McGill, G., Berry, D.: E.O. Wilson’s Life on Earth. Wilson Digital (2012)Google Scholar
  150. 150.
    Woods, A.J.: How are crosstalk and ghosting defined in the stereoscopic literature? In: Proceedings of SPIE, Stereoscopic Displays and Applications XXII, vol. 7863, pp. 78630Z.1–78630Z.12. International Society for Optics and Photonics (2011)Google Scholar
  151. 151.
    Wurtele, E.S., et al.: Meta!Blast: a serious game to explore the complexities of structural and metabolic cell biology. In: Proceedings of the ASME 2010 World Conference on Innovative Virtual Reality, pp. 237–240 (2010)Google Scholar
  152. 152.
    Wurtele, E.S., et al.: MetNet: software to build and model the biogenetic lattice of Arabidopsis. Comp. Funct. Genomics 4(2), 239–245 (2003)CrossRefGoogle Scholar
  153. 153.
    Yang, Y., Wurtele, E.S., Cruz-Neira, C., Dickerson, J.A.: Hierarchical visualization of metabolic networks using virtual reality. In: Proceedings of the 2006 ACM International Conference on Virtual Reality Continuum and Its Applications, pp. 377–381. ACM (2006)Google Scholar
  154. 154.
    Ystad, M.A., et al.: Hippocampal volumes are important predictors for memory function in elderly women. BMC Med. Imaging 9(1), 17.1–17.15 (2009)CrossRefGoogle Scholar
  155. 155.
    Zhao, F., Xie, X.: An overview of interactive medical image segmentation. Ann. BMVA 2013(7), 1–22 (2013)Google Scholar
  156. 156.
    Zhu, L., et al.: Cell where: graphical display of interaction networks organized on subcellular localizations. Nucleic Acids Res. 43, W571–W575 (2015)CrossRefGoogle Scholar
  157. 157.
    zSpace. http://zspace.com. Accessed 20 Apr 2017
  158. 158.

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Tobias Czauderna
    • 1
  • Jason Haga
    • 2
  • Jinman Kim
    • 3
  • Matthias Klapperstück
    • 1
  • Karsten Klein
    • 1
    • 3
    • 4
  • Torsten Kuhlen
    • 5
  • Steffen Oeltze-Jafra
    • 6
  • Björn Sommer
    • 1
    • 4
  • Falk Schreiber
    • 1
    • 4
  1. 1.Monash UniversityClaytonAustralia
  2. 2.National Institute of Advanced Industrial Science and TechnologyTokyoJapan
  3. 3.University of SydneySydneyAustralia
  4. 4.University of KonstanzKonstanzGermany
  5. 5.RWTH Aachen UniversityAachenGermany
  6. 6.Innovation Center Computer Assisted SurgeryUniversity of LeipzigLeipzigGermany

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