Authors:
B. W. van Schooten
1
;
E. M. A. G. van Dijk
2
;
A. Suinesiaputra
3
and
J. H. C. Reiber
3
Affiliations:
1
Human Media Interaction, University of Twente, Netherlands
;
2
University of Twente, Netherlands
;
3
Leiden University Medical Center, Netherlands
Keyword(s):
Volume visualization, Segmentation, Radiology, MRA, Visual cues.
Related
Ontology
Subjects/Areas/Topics:
Abstract Data Visualization
;
Computer Vision, Visualization and Computer Graphics
;
General Data Visualization
;
Interactive Visual Interfaces for Visualization
;
Perception and Cognition in Visualization
;
Visual Representation and Interaction
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 ar
e 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.
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