Browsing by Author "Schultz, Thomas"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    Detection and Visual Analysis of Pathological Abnormalities in Diffusion Tensor Imaging with an Anomaly Lens
    (The Eurographics Association, 2023) Bareth, Marlo; Groeschel, Samuel; Gruen, Johannes; Pretzel, Pablo; Schultz, Thomas; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
    In clinical practice, Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is usually evaluated by visual inspection of grayscale maps of Fractional Anisotropy or mean diffusivity. However, the fact that those maps only contain part of the information that is captured in DT-MRI implies a risk of missing signs of disease. In this work, we propose a visualization system that supports a more comprehensive analysis with an anomaly score that accounts for the full diffusion tensor information. It is computed by comparing the DT-MRI scan of a given patient to a control group of healthy subjects, after spatial coregistration. Moreover, our system introduces an Anomaly Lens which visualizes how a user-specified region of interest deviates from the controls, indicating which aspects of the tensor (norm, anisotropy, mode, rotation) differ most, whether they are elevated or reduced, and whether their covariation matches the covariances within the control group. Applying our system to patients with metachromatic leukodystrophy clearly indicates regions affected by the disease, and permits their detailed analysis.
  • Thumbnail Image
    Item
    Frontmatter: Eurographics Workshop on Visual Computing for Biology and Medicine 2018
    (The Eurographics Association, 2018) Puig Puig, Anna; Schultz, Thomas; Vilanova, Anna; Hotz, Ingrid; Kozlikova, Barbora; Vázquez, Pere-Pau; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
  • Thumbnail Image
    Item
    Open-Box Training of Kernel Support Vector Machines: Opportunities and Limitations
    (The Eurographics Association, 2019) Khatami, Mohammad; Schultz, Thomas; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
    Kernel Support Vector Machines (SVMs) are widely used for supervised classification, and have achieved state-of-the-art performance in numerous applications. We aim to further increase their efficacy by allowing a human operator to steer their training process. To this end, we identify several possible strategies for meaningful human intervention in their training, propose a corresponding visual analytics workflow, and implement it in a prototype system. Initial results from two users, on data from three different domains suggest that, in addition to facilitating better insight into the data and into the classifier's decision process, visual analytics can increase the efficacy of Support Vector Machines when the data available for training has a low number of samples, is unbalanced with respect to the different classes, contains outliers, irrelevant features, or mislabeled samples. However, we also discuss some limitations of improving the efficacy of supervised classification with visual analytics.
  • Thumbnail Image
    Item
    Uncertainty-Guided Semi-Automated Editing of CNN-based Retinal Layer Segmentations in Optical Coherence Tomography
    (The Eurographics Association, 2018) Zadeh, Shekoufeh Gorgi; Wintergerst, Maximilian W. M.; Schultz, Thomas; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
    Convolutional neural networks (CNNs) have enabled dramatic improvements in the accuracy of automated medical image segmentation. Despite this, in many cases, results are still not reliable enough to be trusted ''blindly''. Consequently, a human rater is responsible to check correctness of the final result and needs to be able to correct any segmentation errors that he or she might notice. For a particular use case, segmentation of the retinal pigment epithelium and Bruch's membrane from Optical Coherence Tomography, we develop a system that makes this process more efficient by guiding the rater to segmentations that are most likely to require attention from a human expert, and by developing semi-automated tools for segmentation correction that exploit intermediate representations from the CNN.We demonstrate that our automated ranking of segmentation uncertainty correlates well with a manual assessment of segmentation quality, and with distance to a ground truth segmentation. We also show that, when used together, uncertainty guidance and our semi-automated editing tools decrease the time required for segmentation correction by more than a factor of three.
  • Thumbnail Image
    Item
    VMV 2017 - Vision, Modeling and Visualization: Frontmatter
    (Eurographics Association, 2017) Hullin, Matthias; Klein, Reinhard; Schultz, Thomas; Yao, Angela; Matthias Hullin and Reinhard Klein and Thomas Schultz and Angela Yao