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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/yx6ragzaojavld2pw4coglzb2e" style="color: black;">The Visual Computer</a>
Diffusion Tensor Imaging (DTI) and fiber tracking provide unique insight into the 3D structure of fibrous tissues in the brain. However, the output of fiber tracking contains a significant amount of uncertainty accumulated in the various steps of the processing pipeline. Existing DTI visualization methods do not present these uncertainties to the end-user. This creates a false impression of precision and accuracy that can have serious consequences in applications that rely heavily on risk<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s00371-012-0733-9">doi:10.1007/s00371-012-0733-9</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/p4zndx6m75c5xprodn5gj5etce">fatcat:p4zndx6m75c5xprodn5gj5etce</a> </span>
more »... ment and decisionmaking, such as neurosurgery. On the other hand, adding uncertainty to an already complex visualization can easily lead to information overload and visual clutter. In this work, we propose Illustrative Confidence Intervals to reduce the complexity of the visualization and present only those aspects of uncertainty that are of interest to the user. We look specifically at the uncertainty in fiber shape due to noise and modeling errors. To demonstrate the flexibility of our framework, we compute this uncertainty in two different ways, based on (1) fiber distance and (2) the probability of a fiber connection between two brain regions. We provide the user with interactive tools to define multiple confidence intervals, specify visual styles and explore the uncertainty with a Focus+Context approach. Finally, we have conducted a user evaluation with three neurosurgeons to evaluate the added value of our visualization. R. Brecheisen ( ) · B.M. ter Haar Romeny · A. Vilanova
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