EFFECTIVENESS OF VISUALISATIONS FOR DETECTION OF ERRORS IN SEGMENTATION OF BLOOD VESSELS

B. W. van Schooten, E. M. A. G. van Dijk, A. Suinesiaputra, J. H. C. Reiber

2010

Abstract

Vascular disease diagnosis often requires a precise segmentation of the vessel lumen. When 3D (Magnetic Resonance Angiography, MRA, or Computed Tomography Angiography, CTA) imaging is available, this can be done automatically, but occasional errors are inevitable. So, the segmentation has to be checked by clinicians. This requires appropriate visualisation techniques. A number of visualisation techniques exist, but there has been little in the way of user studies that compare the different alternatives. In this study we examine how users interact with several basic visualisations, when performing a visual search task, checking vascular segmentation correctness of segmented MRA data. These visualisations are: direct volume rendering (DVR), isosurface rendering, and curved planar reformatting (CPR). Additionally, we examine if visual highlighting of potential errors can help the user find errors, so a fourth visualisation we examine is DVR with visual highlighting. Our main findings are that CPR performs fastest but has higher error rate, and there are no significant differences between the other three visualisations. We did find that visual highlighting actually has slower performance in early trials, suggesting that users learned to ignore them.

References

  1. Achenbach, S., Moshage, W., Ropers, D., and Bachmann, K. (1998). Curved multiplanar reconstructions for the evaluation of contrast-enhanced electron-beam CT of the coronary arteries. American Journal of Roentgenology, pages 895-899.
  2. Bade, R., Ritter, F., and Preim, B. (2005). Usability comparison of mouse-based interaction techniques for predictable 3D rotation. In 5th international symposium on smart graphics: SG 2005, pages 138-150. Springer.
  3. Boskamp, T., Rinck, D., Link, F., Kümmerlen, B., Stamm, G., and Mildenberger, P. (2004). New vessel analysis tool for morphometric quantification and visualization of vessels in CT and MR imaging data sets. Radiographics, 24(1):287-297.
  4. Dixon, S. R., Wickens, C. D., and McCarley, J. S. (2007). On the independence of compliance and reliance: are automation false alarms worse than misses? Human factors, 49(4):564-72.
  5. Fisher, D. L. and Tan, K. C. (1989). Visual displays: The highlighting paradox. Human Factors, 31(1):17-30.
  6. Freer, T. W. and Ulissey, J. M. (2001). Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center. Radiology, 220:781-786.
  7. Hong, W., Qiu, F., and Kaufman, A. (2006). A pipeline for computer aided polyp detection. IEEE Transactions on Visualization and Computer Graphics, 12(5):861- 868.
  8. Kanitsar, A. (2004). Curved Planar Reformation for Vessel Visualization. PhD thesis, Institute of Computer Graphics and Algorithms, Vienna University of Technology, Favoritenstrasse 9-11/186, A-1040 Vienna, Austria.
  9. Levinski, K., Sourin, A., and Zagorodnov, V. (2009). 3D visualization and segmentation of brain MRI data. In GRAPP 2009, pages 111-118.
  10. Levy, J. H., Broadhurst, R. R., Ray, S., Chaney, E. L., and Pizer, S. M. (2007). Signaling local non-credibility in an automatic segmentation pipeline. In Proceedings of the International Society for Optical Engineering meetings on Medical Imaging, Volume 6512.
  11. L ópez-Aligué, F. J., Acevedo-Sotoca, I., García-Manso, A., García-Orellana, C. J., and Gallardo-Caballero, R. (2004). Microcalcifications detection in digital mammograms. In EMBC 2004.
  12. Maltz, M. and Shinar, D. (2003). New alternative methods of analyzing human behavior in cued target acquisition. Human Factors, 45(2):281-295.
  13. Mueller, D. C., Maeder, A. J., and O'Shea, P. J. (2005). Enhancing direct volume visualisation using perceptual properties. In Proc. SPIE, Vol. 5744, pages 446-454.
  14. Rolland, J. P., Muller, K. E., and Helvig, C. S. (1995). Visual search in medical images: a new methodology to quantify saliency. In Proc. SPIE Vol. 2436, pages 40- 48.
  15. Suinesiaputra, A., de Koning, P. J., Zudilova-Seinstra, E. V., Reiber, J. H. C., and van der Geest, R. J. (2009). A 3D MRA segmentation method based on tubular NURBS model. In International Society for Magnetic Resonance in Medicine 2009, Honolulu, Hawaii.
  16. Tamborello, F. P. and Byrne, M. D. (2007). Adaptive but non-optimal visual search behavior with highlighted displays. Cognitive Systems Research, 8(3):182-191.
  17. van Schooten, B. W., van Dijk, E. M. A. G., ZudilovaSeinstra, E. V., de Koning, P. J. H., and Reiber, J. H. C. (2009). Evaluating visualisation and navigation techniques for interpretation of MRA data. In GRAPP 2009, pages 405-408.
  18. Wang, Y., Gao, X., and Li, J. (2007). A feature analysis approach to mass detection in mammography based on RF-SVM. In ICIP 07, pages 9-12.
  19. Wickens, C. D. and Andre, A. D. (1990). Proximity compatibility and information display: Effects of color, space, and objectness on information integration. Human Factors, 32(1):61-77.
  20. Yeh, M. and Wickens, C. D. (2001). Display signaling in augmented reality: Effects of cue reliability and image realism on attention allocation and trust calibration. Human Factors, 43(3):355-365.
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Paper Citation


in Harvard Style

W. van Schooten B., van Dijk E., Suinesiaputra A. and Reiber J. (2010). EFFECTIVENESS OF VISUALISATIONS FOR DETECTION OF ERRORS IN SEGMENTATION OF BLOOD VESSELS . In Proceedings of the International Conference on Imaging Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2010) ISBN 978-989-674-027-6, pages 77-84. DOI: 10.5220/0002821800770084


in Bibtex Style

@conference{ivapp10,
author={B. W. van Schooten and E. M. A. G. van Dijk and A. Suinesiaputra and J. H. C. Reiber},
title={EFFECTIVENESS OF VISUALISATIONS FOR DETECTION OF ERRORS IN SEGMENTATION OF BLOOD VESSELS},
booktitle={Proceedings of the International Conference on Imaging Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2010)},
year={2010},
pages={77-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002821800770084},
isbn={978-989-674-027-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Imaging Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2010)
TI - EFFECTIVENESS OF VISUALISATIONS FOR DETECTION OF ERRORS IN SEGMENTATION OF BLOOD VESSELS
SN - 978-989-674-027-6
AU - W. van Schooten B.
AU - van Dijk E.
AU - Suinesiaputra A.
AU - Reiber J.
PY - 2010
SP - 77
EP - 84
DO - 10.5220/0002821800770084