Ulrik Günther

Jan Tiemann, Matthew McGinity, Ulrik Günther

In contemporary biology and medicine, 3D microscopy is one of the most widely-used techniques for imaging and manipulation of various kinds of samples. Navigating such a micrometer-sized, 3-dimensional sample under the microscope -- e.g. to find relevant imaging regions -- can pose a tedious challenge for the experimenter. In this paper, we examine whether 2D desktop, 3D desktop, or Virtual Reality (VR) interfaces provide the best user experience and performance for the exploration of 3D samples. We invited 12 skilled microscope operators to perform two different exploration tasks in 2D, 3D and VR and compared all conditions in terms speed, usability, and completion. Our results show a clear benefit when using VR -- in terms of task efficiency, usability, and user acceptance. Intriguingly, while VR outperformed desktop 2D and 3D in all scenarios, 3D desktop did not outperform 2D desktop.

Ulrik Günther, Julián Méndez, Gabriela Molina León et al.

We propose a hybrid half-day workshop at IEEE VIS 2026, calling for participation from visualization researchers and science fiction creators in order to develop a systematic understanding of the two-way relationship these communities have long shared. We invite submissions of creative formats showcasing connections and inspiring future research. Our workshop plan includes a keynote, lightning talks, brainstorming, cross-community critique, affinity mapping, and discussion around identified themes.

Ulrik Günther, Kyle I. S. Harrington, Raimund Dachselt et al.

We present Bionic Tracking, a novel method for solving biological cell tracking problems with eye tracking in virtual reality using commodity hardware. Using gaze data, and especially smooth pursuit eye movements, we are able to track cells in time series of 3D volumetric datasets. The problem of tracking cells is ubiquitous in developmental biology, where large volumetric microscopy datasets are acquired on a daily basis, often comprising hundreds or thousands of time points that span hours or days. The image data, however, is only a means to an end, and scientists are often interested in the reconstruction of cell trajectories and cell lineage trees. Reliably tracking cells in crowded three-dimensional space over many timepoints remains an open problem, and many current approaches rely on tedious manual annotation and curation. In our Bionic Tracking approach, we substitute the usual 2D point-and-click annotation to track cells with eye tracking in a virtual reality headset, where users simply have to follow a cell with their eyes in 3D space in order to track it. We detail the interaction design of our approach and explain the graph-based algorithm used to connect different time points, also taking occlusion and user distraction into account. We demonstrate our cell tracking method using the example of two different biological datasets. Finally, we report on a user study with seven cell tracking experts, demonstrating the benefits of our approach over manual point-and-click tracking.