Browsing by Author "Schultz, Thomas"

Now showing 1 - 4 of 4
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    Interactive Classification of Multi-Shell Diffusion MRI With Features From a Dual-Branch CNN Autoencoder
    (The Eurographics Association, 2020) Torayev, Agajan; Schultz, Thomas; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
    Multi-shell diffusion MRI and Diffusion Spectrum Imaging are modern neuroimaging modalities that acquire diffusion weighted images at a high angular resolution, while also probing varying levels of diffusion weighting (b values). This yields large and intricate data for which very few interactive visualization techniques are currently available. We designed and implemented the first system that permits an interactive, iteratively refined classification of such data, which can serve as a foundation for isosurface visualizations and direct volume rendering. Our system leverages features learned by a Convolutional Neural Network. CNNs are state of the art for representation learning, but training them is too slow for interactive use. Therefore, we combine a computationally efficient random forest classifier with autoencoder based features that can be pre-computed by the CNN. Since features from existing CNN architectures are not suitable for this purpose, we design a specific dual-branch CNN architecture, and carefully evaluate our design decisions. We demonstrate that our approach produces more accurate classifications compared to learning with raw data, established domain-specific features, or PCA dimensionality reduction.
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    Interactive Formation of Statistical Hypotheses in Diffusion Tensor Imaging
    (The Eurographics Association, 2019) Abbasloo, Amin; Wiens, Vitalis; Schmidt-Wilcke, Tobias; Sundgren, Pia; Klein, Reinhard; Schultz, Thomas; Kozlíková, Barbora and Linsen, Lars and Vázquez, Pere-Pau and Lawonn, Kai and Raidou, Renata Georgia
    When Diffusion Tensor Imaging (DTI) is used in clinical studies, statistical hypothesis testing is the standard approach to establish significant differences between groups, such as patients and healthy controls. However, diffusion tensors contain six degrees of freedom, and the most commonly used univariate tests reduce them to a single scalar, such as Fractional Anisotropy. Multivariate tests that account for the full tensor information have been developed, but have not been widely adopted in practice. Based on analyzing the limitations of existing univariate and multivariate tests, we argue that it is beneficial to use a more flexible, steerable test. Therefore, we introduce a test that can be customized to include any subset of tensor attributes that are relevant to the analysis task at hand. We also present a visual analytics system that supports the exploratory task of customizing it to a specific scenario. Our system closely integrates quantitative analysis with suitable visualizations. It links spatial and abstract views to reveal clusters of strong differences, to relate them to the affected anatomical structures, and to visually compare the results of different tests. A use case is presented in which our system leads to the formation of several new hypotheses about the effects of systemic lupus erythematosus on water diffusion in the brain.
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    Reducing Model Uncertainty in Crossing Fiber Tractography
    (The Eurographics Association, 2021) Gruen, Johannes; Voort, Gemma van der; Schultz, Thomas; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
    Diffusion MRI (dMRI) tractography permits the non-invasive reconstruction of major white matter tracts, and is therefore widely used in neurosurgical planning and in neuroscience. However, it is affected by various sources of uncertainty. In this work, we consider the model uncertainty that arises in crossing fiber tractography, from having to select between alternative mathematical models for the estimation of multiple fiber orientations in a given voxel. This type of model uncertainty is a source of instability in dMRI tractography that has not received much attention so far. We develop a mathematical framework to quantify it, based on computing posterior probabilities of competing models, given the local dMRI data. Moreover, we explore a novel strategy for crossing fiber tractography, which computes tracking directions from a consensus of multiple mathematical models, each one contributing with a weight that is proportional to its probability. Experiments on different white matter tracts in multiple subjects indicate that reducing model uncertainty in this way increases the accuracy of crossing fiber tractography.
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    VCBM 2021: Frontmatter
    (The Eurographics Association, 2021) Oeltze-Jafra, Steffen; Smit, Noeska N.; Sommer, Björn; Nieselt, Kay; Schultz, Thomas; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas