Real-world sculptures that display patient imaging data for anatomical education purposes have seen a recent resurgence through the field of data physicalization. In this paper, we describe an automated process for the computer-assisted generation of sculptures that can be employed for anatomical education among the general population. We propose a workflow that supports non-expert users to generate and physically display volumetric medical data in a visually appealing and engaging way. Our approach generates slide-based, interactive sculptures—called volograms—that resemble holograms of underlying medical data. The volograms are made out of affordable and readily available materials (e.g., transparent foils and cardboard) and can be produced through commonly available means. To evaluate the educational value of the proposed approach with our target audience, we assess the volograms, as opposed to classical, on-screen medical visualizations in a user study. The results of our study, while highlighting current weaknesses of our physicalization, also point to interesting future directions.
Pediatric brain tumor radiotherapy research is investigating how radiation influences the development and function of a patient’s brain. To better understand how brain growth is affected by the treatment, the brain structures of the patient need to be explored and analyzed pre- and post-treatment. In this way, anatomical changes are observed over a long period and are assessed as potential early markers of cognitive or functional damage. In this early work, we propose an automated approach for the visual assessment of the growth prediction of brain structures in pediatric brain tumor radiotherapy patients. Our approach reduces the need for re-segmentation and the time required for it. We employ as a basis pre-treatment Computed Tomography (CT) scans with manual delineations (i.e., segmentation masks) of specific brain structures of interest. These pre-treatment masks are used as initialization, to predict the corresponding masks on multiple post-treatment follow-up Magnetic Resonance (MR) images, using an active contour model approach. For the accuracy quantification of the automatically predicted posttreatment masks, a support vector regressor (SVR) with features related to geometry, intensity, and gradients is trained on the pre-treatment data. Finally, a distance transform is employed to calculate the distances between pre- and post-treatment data and to visualize the predicted growth of a brain structure, along with its respective accuracy. Although segmentations of larger structures are more accurately predicted, the growth behavior of all structures is learned correctly, as indicated by the SVR results. This suggests that our pipeline is a positive initial step for the visual assessment of brain structure growth prediction.
In recent years, digital outcrop models have become a popular tool to carry out geological investigations on the computer. These high-resolution, 3-dimensional models of outcrops are also created for the exploration of Mars. With specialized software, geologists can annotate geological attributes on digital outcrop models, such as the boundaries between different rock layers. After annotating, geologists create logs, a graphic description of the rock layers. In order to establish a geological model of a larger region, corresponding layers are correlated in multiple logs. The correlated layers of the logs are graphically linked in a correlation panel. Creating correlation panels is very time-consuming, and they are usually created by hand with drawing programs. Due to this restriction, the diagrams are created at the end of the interpretation process, in order to avoid time-consuming editing afterwards. When switching to a drawing program, the connection between the original data and the encoded data in the diagram is also lost. This work is part of a design study with the aim of automating the creation of correlation panels, and turning a static illustration into an interactive application that can be integrated into the interpretation process. In this work, after a short introduction to the exploration of Mars with the help of geology, we analyse published correlation panels to explore the design space of these illustrations. In addition to that analysis we conducted workshops and a research stay at Imperial College London with our domain collaborators. Using the information gained from the analysis and our collaborators, we describe possible design choices, and extract the minimum requirements for a prototype. The prototype created in the course of this work was later extended and presented in a paper that encompasses the whole design study.
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Supervisor: Eduard Gröller
Institute of Visual Computing & Human-Centered Technology
Favoritenstr. 9-11 / E193-02
Austria - Europe