Evaluating Visualisations and Automatic Warning Cues for Visual Search in Vascular Images

Evaluating Visualisations and Automatic Warning Cues for Visual Search in Vascular Images

Boris W. van Schooten (University of Twente, The Netherlands), Betsy van Dijk (University of Twente, The Netherlands), Avan Suinesiaputra (University of Auckland, New Zealand), Anton Nijholt (University of Twente, The Netherlands) and Johan H. C. Reiber (Leiden University Medical Center, The Netherlands)
DOI: 10.4018/978-1-4666-1628-8.ch005
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Visual search is a task that is performed in various application domains. The authors examine it in the domain of radiological analysis of 3D vascular images. They compare several major visualisations used in this domain, and study the possible benefits of automatic warning systems that highlight the sections that may contain visual targets and hence require the user’s attention. With help of a literature study, the authors present some theory about what result can be expected given the accuracy of a particular visual cue. They present the results of two experiments, in which they find that the Curved Planar Reformation visualisation, which presents a cross-section based on knowledge about the position of the blood vessel, is significantly more efficient than regular 3D visualisations, and that automatic warning systems that produce false alarms could work if they do not miss targets.
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In the following sections, we examine visual search in the area of medical imaging, more specifically, 3D vascular imaging. Vascular disease diagnosis can be done effectively by means of 3D imaging techniques such as Magnetic Resonance Angiography (MRA) and Computed Tomography Angiography (CTA). The most common vascular diseases that these imaging techniques help diagnose are stenoses (abnormal narrowings in blood vessels) and aneurysms (abnormal widening or ballooning of blood vessels). We focus on Contrast Enhanced MRA as this is the area of our current research (Suinesiaputra, et al., 2009). CE-MRA involves injection of a contrast agent in the blood stream, making the relevant blood vessels show up as high-density areas on the MRA scan. The output of CE-MRA is a 3D density field (typically around 128x128x128 pixels), with higher densities representing blood flow.

With help of 3D imaging techniques, the thickness of the inside of the vessel (the vessel lumen) can be determined precisely, and assessed quantitatively. This is a very attractive tool in diagnosis. Determination of the vessel lumen is called segmentation. It results in a 3D (usually tubular) surface representing the vessel lumen circumference. Since manual segmentation is time-consuming, automatic segmentation is proposed (Boskamp, et al., 2004; Suinesiaputra, et al., 2009). Automatic segmentation typically first constructs a pathline, which is a line that goes through the lumen of the vessel to be segmented (Boskamp, et al., 2004). From the pathline, a triangle mesh is constructed indicating the lumen. The pathline is also useful as a basis for “intelligent” visualisations.

In automatic segmentation, occasional errors are currently inevitable. Therefore, clinicians have to detect (and correct) errors in the automatic segmentations by means of visual search. This visual search task is the task that we examine in this chapter.

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