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Uncertainty visualization for interactive assessment of stenotic regions in vascular structures

: Ristovski, G.; Matute, J.; Wehrum, T.; Harloff, A.; Hahn, H.K.; Linsen, L.


Computers and Graphics 69 (2017), S.116-130
ISSN: 0097-8493
Fraunhofer MEVIS ()

Stenosis refers to the thinning of the inner surface (lumen) of vascular structures. Detecting stenoses and correctly estimating their degree is crucial in clinical settings for proper treatment planning. Such a planning involves a visual assessment, which in case of vascular structures is frequently based on 3D visual representations of the vessels. However, since vessel segmentation is affected by various sources of errors and noise in the imaging and image processing pipeline, it is crucial to capture and visually convey the uncertainty in a 3D visual representation. Moreover, it is crucial to quantify how much this uncertainty affects the calculated stenotic degree, since different severities lead to different treatments. We propose a novel approach for visualizing the shape deviation of different probability levels in vascular data, where the probability levels are computed from a probabilistic segmentation approach. Our non-obstructive visual encoding is based on rendering a single opaque surface representing a probability level of the cumulative distribution function around the vessels’ centerline. The surface rendering is enhanced with cumulative information about other levels. To do so, we traverse the probability space by applying an iterative projection method both inwards and outward until we reach surface variability within a given margin. We capture the shape variability between the different probability levels using the lengths of the projection lines, the change in angular directions, and the distortion of a parametrization. They are visually encoded using color and texture mapping. Furthermore, we allow for an interactive selection of a region of interest that automatically calculates the stenotic degree and how much the uncertainty affects the most likely result. We analyze our approach in comparison to state-of-the-art methods with medical experts in a study using both real magnetic resonance (MR) and computed tomography (CT) angiography data of vertebral arteries with stenoses as well as on MR angiography data with synthetically added stenoses and stenotic uncertainties. We evaluate how well our approach can guide medical experts in their assessment of the uncertainty in vertebral stenoses.