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ParaGlyder: Probe-driven Interactive Visual Analysis for Multiparametric Medical Imaging Data

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Advances in Computer Graphics (CGI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12221))

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Abstract

Multiparametric imaging in cancer has been shown to be useful for tumor detection and may also depict functional tumor characteristics relevant for clinical phenotypes. However, when confronted with datasets consisting of multiple values per voxel, traditional reading of the imaging series fails to capture complicated patterns. These patterns of potentially important imaging properties of the parameter space may be critical for the analysis, but standard approaches do not deliver sufficient details. Therefore, in this paper, we present an approach that aims to enable the exploration and analysis of such multiparametric studies using an interactive visual analysis application to remedy the trade-offs between details in the value domain and in spatial resolution. This may aid in the discrimination between healthy and cancerous tissue and potentially highlight metastases that evolved from the primary tumor. We conducted an evaluation with eleven domain experts from different fields of research to confirm the utility of our approach.

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References

  1. Bangor, A., Kortum, P., Miller, J.: Determining what individual SUS scores mean: adding an adjective rating scale. J. Usability Stud. 4(3), 114–123 (2009)

    Google Scholar 

  2. Berg, A., et al.: Tissue and imaging biomarkers for hypoxia predict poor outcome in endometrial cancer. Oncotarget 7(43), 69844–69856 (2016). https://doi.org/10.18632/oncotarget.12004

    Article  Google Scholar 

  3. Brooke, J.: SUS-a quick and dirty usability scale. In: Jordan, P., Thomas, B., McClelland, I., Weerdmeester, B. (eds.) Usability Evaluation In Industry, pp. 266–290. CRC Press, Boca Raton (2004)

    Google Scholar 

  4. Bruckner, S., et al.: BrainGazer - visual queries for neurobiology research. IEEE Trans. Visual. Comput. Graphics 15, 1497–504 (2009). https://doi.org/10.1109/TVCG.2009.121

    Article  Google Scholar 

  5. Clark, K., et al.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045–1057 (2013). https://doi.org/10.1007/s10278-013-9622-7

    Article  Google Scholar 

  6. Fasmer, K.E., et al.: Preoperative quantitative dynamic contrast-enhanced MRI and diffusion-weighted imaging predict aggressive disease in endometrial cancer. Acta Radiol. 59(8), 1010–1017 (2018). https://doi.org/10.1177/0284185117740932

    Article  Google Scholar 

  7. Friendly, M.: A.-M. Guerry’s Moral Statistics of France: challenges for multivariable spatial analysis. Stat. Sci. 22(3), 368–399 (2007). https://doi.org/10.1214/07-STS241

    Article  MathSciNet  MATH  Google Scholar 

  8. Gleicher, M., Albers, D., Walker, R., Jusufi, I., Hansen, C.D., Roberts, J.C.: Visual comparison for information visualization. Inf. Vis. 10(4), 289–309 (2011). https://doi.org/10.1177/1473871611416549

    Article  Google Scholar 

  9. Haldorsen, I.S., et al.: Increased microvascular proliferation is negatively correlated to tumour blood flow and is associated with unfavourable outcome in endometrial carcinomas. Br. J. Cancer 110(1), 107–114 (2014). https://doi.org/10.1038/bjc.2013.694

    Article  Google Scholar 

  10. Haldorsen, I.S., et al.: Dynamic contrast-enhanced MRI in endometrial carcinoma identifies patients at increased risk of recurrence. Eur. Radiol. 23(10), 2916–2925 (2013). https://doi.org/10.1007/s00330-013-2901-3

    Article  Google Scholar 

  11. Haldorsen, I.S., Salvesen, H.B.: What is the best preoperative imaging for endometrial cancer? Curr. Oncol. Rep. 18(4), 25 (2016). https://doi.org/10.1007/s11912-016-0506-0

    Article  Google Scholar 

  12. Jäckle, D., Fuchs, J., Keim, D.A.: Star glyph insets for overview preservation of multivariate data. In: IS and T International Symposium on Electronic Imaging Science and Technology, pp. 1–9 (2016). https://doi.org/10.2352/issn.2470-1173.2016.1.vda-506

  13. Jönsson, D., et al.: A visual environment for hypothesis formation and reasoning in studies with fMRI and multivariate clinical data. In: Kozlíková, B., Linsen, L., Vázquez, P.P., Lawonn, K., Raidou, R.G. (eds.) Eurographics Workshop on Visual Computing for Biology and Medicine. The Eurographics Association (2019). https://doi.org/10.2312/vcbm.20191232

  14. Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.W.: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29(1), 196–205 (2010). https://doi.org/10.1109/TMI.2009.2035616

    Article  Google Scholar 

  15. Klippel, A., Hardisty, F., Weaver, C.: Star plots: how shape characteristics influence classification tasks. Cartogr. Geogr. Inf. Sci. 36(2), 149–163 (2009). https://doi.org/10.1559/152304009788188808

    Article  Google Scholar 

  16. Lawonn, K., Smit, N., Bühler, K., Preim, B.: A survey on multimodal medical data visualization. Comput. Graph. Forum 37(1), 413–438 (2017). https://doi.org/10.1111/cgf.13306

    Article  Google Scholar 

  17. Malik, M.M., Heinzl, C., Gröller, M.E.: Comparative visualization for parameter studies of dataset series. IEEE Trans. Visual. Comput. Graphics 16(5), 829–840 (2010). https://doi.org/10.1109/TVCG.2010.20

    Article  Google Scholar 

  18. Mlejnek, M., et al.: Profile flags: a novel metaphor for probing of t2 maps. In: Silva, C.T., Gröller, E., Rushmeier, H. (eds.) Proceedings of IEEE Visualization 2005, pp. 599–606. IEEE CS, October 2005

    Google Scholar 

  19. Munzner, T.: A nested model for visualization design and validation. IEEE Trans. Visual Comput. Graphics 15(6), 921–928 (2009)

    Article  Google Scholar 

  20. Opach, T., Popelka, S., Dolezalova, J., Rød, J.K.: Star and polyline glyphs in a grid plot and on a map display: which perform better? Cartogr. Geogr. Inf. Sci. 45(5), 400–419 (2018). https://doi.org/10.1080/15230406.2017.1364169

    Article  Google Scholar 

  21. Peng, W., Ward, M.O., Rundensteiner, E.A.: Clutter reduction in multi-dimensional data visualization using dimension reordering. In: Proceedings - IEEE Symposium on Information Visualization, INFO VIS, pp. 89–96 (2004). https://doi.org/10.1109/INFVIS.2004.15

  22. Ropinski, T., Oeltze, S., Preim, B.: Visual computing in biology and medicine: survey of glyph-based visualization techniques for spatial multivariate medical data. Comput. Graph. 35(2), 392–401 (2011). https://doi.org/10.1016/j.cag.2011.01.011

    Article  Google Scholar 

  23. Schmainda, K., Prah, M.: Data from brain-tumor-progression (2018). https://doi.org/10.7937/K9/TCIA.2018.15quzvnb

  24. Smit, N.N., Kraima, A.C., Jansma, D., Ruiter, M.C.d., Botha, C.P.: A unified representation for the model-based visualization of heterogeneous anatomy data. In: Meyer, M., Weinkaufs, T. (eds.) EuroVis - Short Papers. The Eurographics Association (2012). https://doi.org/10.2312/PE/EuroVisShort/EuroVisShort2012/085-089

  25. Smit, N.N., Haneveld, B.K., Staring, M., Eisemann, E., Botha, C.P., Vilanova, A.: RegistrationShop: an interactive 3D medical volume registration system. In: Viola, I., Buehler, K., Ropinski, T. (eds.) Eurographics Workshop on Visual Computing for Biology and Medicine. The Eurographics Association (2014). https://doi.org/10.2312/vcbm.20141193

  26. Stoppel, S., Hodneland, E., Hauser, H., Bruckner, S.: Graxels: information rich primitives for the visualization of time-dependent spatial data. In: Eurographics Workshop on Visual Computing for Biology and Medicine, pp. 183–192, September 2016. https://doi.org/10.2312/vcbm.20161286

  27. Wickham, H., Hofmann, H., Wickham, C., Cook, D.: Glyph-maps for visually exploring temporal patterns in climate data and models. Environmetrics 23(5), 382–393 (2012). https://doi.org/10.1002/env.2152

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Informatics, University of Bergen, Bergen, Norway

    Eric Mörth, Stefan Bruckner & Noeska N. Smit

  2. Mohn Medical Imaging and Visualization Centre, Haukeland University Hospital, Bergen, Norway

    Eric Mörth, Ingfrid S. Haldorsen, Stefan Bruckner & Noeska N. Smit

  3. Department of Clinical Medicine, University of Bergen, Bergen, Norway

    Ingfrid S. Haldorsen

Authors
  1. Eric Mörth
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  2. Ingfrid S. Haldorsen
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  3. Stefan Bruckner
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  4. Noeska N. Smit
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Corresponding author

Correspondence to Eric Mörth .

Editor information

Editors and Affiliations

  1. University of Geneva, Geneva, Switzerland

    Nadia Magnenat-Thalmann

  2. University of Crete, Heraklion, Greece

    Constantine Stephanidis

  3. University of Macau, Macau, China

    Enhua Wu

  4. Swiss Federal Institute of Technology, Lausanne, Switzerland

    Daniel Thalmann

  5. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  6. University of Sydney, Sydney, Australia

    Jinman Kim

  7. University of Crete, Heraklion, Greece

    George Papagiannakis

  8. University of Calgary, Calgary, AB, Canada

    Marina Gavrilova

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Mörth, E., Haldorsen, I.S., Bruckner, S., Smit, N.N. (2020). ParaGlyder: Probe-driven Interactive Visual Analysis for Multiparametric Medical Imaging Data. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2020. Lecture Notes in Computer Science(), vol 12221. Springer, Cham. https://doi.org/10.1007/978-3-030-61864-3_29

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  • DOI: https://doi.org/10.1007/978-3-030-61864-3_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61863-6

  • Online ISBN: 978-3-030-61864-3

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