Metrics for Uncertainty Analysis and Visualization of Diffusion Tensor Images

Jiao, Fangxiang; Phillips, Jeff M.; Stinstra, J.G.; Krüger, Jens; Varma, Raj; Hsu, Edward W.; Korenberg, Julie R.; Johnson, Chris R.

doi:10.1007/978-3-642-15699-1_19

Cited by 14 publications

(10 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first set of methods display the data as a glyph representation [35, 41], which indicates the fiber directional or fiber crossing uncertainty at a given location. The other set of approaches track fibers under uncertain conditions, giving either a color map for confidence [11] or an envelop of potential fiber routes [37]. Figure 4 shows a recent effort by [36] to use volume rendering with multi-dimensional transfer functions to visualize the uncertainty associated with high angular resolution diffusion imaging (HARDI).…”

Section: Tensor Fieldsmentioning

confidence: 99%

From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches

Potter

Rosen

Johnson

2012

IFIP Advances in Information and Communication Technology

Self Cite

158

View full text Add to dashboard Cite

Quantifying uncertainty is an increasingly important topic across many domains. The uncertainties present in data come with many diverse representations having originated from a wide variety of disciplines. Communicating these uncertainties is a task often left to visualization without clear connection between the quantification and visualization. In this paper, we first identify frequently occurring types of uncertainty. Second, we connect those uncertainty representations to ones commonly used in visualization. We then look at various approaches to visualizing this uncertainty by partitioning the work based on the dimensionality of the data and the dimensionality of the uncertainty. We also discuss noteworthy exceptions to our taxonomy along with future research directions for the uncertainty visualization community.

show abstract

Section: Tensor Fieldsmentioning

confidence: 99%

From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches

Potter

Rosen

Johnson

2012

IFIP Advances in Information and Communication Technology

Self Cite

158

View full text Add to dashboard Cite

show abstract

“…As Jiao et al [13] point out distance measures that are based on averages, such as the mean of closest-point distances, may over or underestimate the true distance due to streamline discretization problems or complex streamline configurations. The closest end-points distance may surely over-simplify the situation.…”

Section: Discussionmentioning

confidence: 99%

“…Fiber distance measures have been extensively described in earlier work on fiber clustering [20] and comparison of fiber tracking algorithms [13]. The choice of distance measure is entirely dependent on the application within which it is used.…”

Section: Wild Bootstrap and Fiber Distancementioning

confidence: 99%

Illustrative uncertainty visualization of DTI fiber pathways

et al. 2012

View full text Add to dashboard Cite

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 assessment 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

show abstract

“…Let d : T × T ↦ ℝ + be a distance function between streamlines. Several anatomically meaningful distances have been proposed in the literature, based on the idea that streamlines with similar path and shape belong to the same anatomical structure, see Gerig et al (2004), Corouge et al (2004), Zhang et al (2008), and Jiao et al (2010). In this work we use the commonly adopted mean average minimum (MAM) distance, a modified Hausdorff distance sometimes called also mean closest point distance (see Corouge et al, 2004):

\begin{matrix} left \\ d_{M A M} false(s, s^{'} false) = \frac{1}{2} false(D false(s, s^{'} false) + D false(s^{'}, s false) false) \end{matrix}

where

D (s, s^{'}) = \frac{1}{n_{s}} {falsefalse}_{\sum}^{i = 1} d ({bold x}_{i}, s^{'})

, and

d (bold x, s^{'}) = {min}_{j = 1, \dots, n_{s^{'}}} | | bold x - {bold x}_{j}^{'} | |_{2}

.…”

Section: Methodsmentioning

confidence: 99%

Alignment of Tractograms As Graph Matching

Olivetti¹,

Sharmin²,

Avesani³

2016

Front. Neurosci.

View full text Add to dashboard Cite

The white matter pathways of the brain can be reconstructed as 3D polylines, called streamlines, through the analysis of diffusion magnetic resonance imaging (dMRI) data. The whole set of streamlines is called tractogram and represents the structural connectome of the brain. In multiple applications, like group-analysis, segmentation, or atlasing, tractograms of different subjects need to be aligned. Typically, this is done with registration methods, that transform the tractograms in order to increase their similarity. In contrast with transformation-based registration methods, in this work we propose the concept of tractogram correspondence, whose aim is to find which streamline of one tractogram corresponds to which streamline in another tractogram, i.e., a map from one tractogram to another. As a further contribution, we propose to use the relational information of each streamline, i.e., its distances from the other streamlines in its own tractogram, as the building block to define the optimal correspondence. We provide an operational procedure to find the optimal correspondence through a combinatorial optimization problem and we discuss its similarity to the graph matching problem. In this work, we propose to represent tractograms as graphs and we adopt a recent inexact sub-graph matching algorithm to approximate the solution of the tractogram correspondence problem. On tractograms generated from the Human Connectome Project dataset, we report experimental evidence that tractogram correspondence, implemented as graph matching, provides much better alignment than affine registration and comparable if not better results than non-linear registration of volumes.

show abstract

Metrics for Uncertainty Analysis and Visualization of Diffusion Tensor Images

Cited by 14 publications

References 13 publications

From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches

From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches

Illustrative uncertainty visualization of DTI fiber pathways

Alignment of Tractograms As Graph Matching

Contact Info

Product

Resources

About