@article {850,
	title = {Towards Closing the Gap of Medical Visualization Research and Clinical Daily Routine},
	journal = {EG Digital Library},
	year = {2020},
	abstract = {Medical visualization papers are constantly published throughout the last years, but many never make their way into clinical daily routine. In this manuscript we aim to examine the gap between visualization research and clinical daily routine and suggest a mechanism that can lead towards closing this gap. We first identify the actors involved in developing new medical visualization approaches and their different views in this process. Then we develop a software development process unifying all actors and their needs. In addition, we collect further barriers in the medical software development process.},
	author = {Maack, Robin and Saur, Dorothee and Hagen, Hans and Scheuermann, Gerik and Christina Gillmann}
}
@article {851,
	title = {Exploring Cinema Databases using multi-dimensional Image Measures},
	journal = {LEVIA 2019},
	year = {2019},
	abstract = {Exa-scale simulations can be hard to analyze because it is nearly impossible to store all computed time-steps and other parameters. The
Cinema Database provides a storage-saving solution, that captures images of each simulation time-step from a variety of camera angles.
Still, the resulting number of images can be overwhelming and it is hard to find interesting images and features for further analysis. We
present a zoom based approach where users can utilize arbitrary image measures to explore interesting images and further analyze their
behaviour in detail. We showed the effectiveness of our approach by
providing two real world Cinema datasets.},
	author = {Maack, Robin and Rogers, David and Hagen, Hans and Scheuermann, Gerik}
}
@conference {852,
	title = {Uncertainty-aware Ramachandran Plots},
	booktitle = {PacificVis 2019},
	year = {2019},
	abstract = {Ramachandran Plots are an important tool for researchers in biochemistry to examine the stability of a molecule. In these plots, dihedral (torsion) angles of the protein{\textquoteright}s backbone are visualized on a plane, where different areas are known to be stable configurations.
Unfortunately, the underlying atom positions are affected by uncertainty, which is usually captured and expressed using the b-value.
For classic Ramachandran Plots, this uncertainty is not propagated when computing the dihedral angles and neglected when visualizing
a Ramachandran Plot. To solve this problem, this paper presents an extended version of the Ramachandran Plot, which allows to
communicate the uncertainty of atom positions along the computation of dihedral angles and an intuitive visualization. We show the effectiveness of the presented approach by examining different Ramachandran Plots for molecules and show how the inclusion of uncertainty helps biochemistry researchers to determine the stability of a protein with higher accuracy.},
	author = {Maack, Robin and Hagen, Hans and Gillmann, Christina}
}
@conference {854,
	title = {Evaluation of the uncertainty-awareness principle using a tumor detection scenario},
	booktitle = {Proceedings of the VisGuides: 2nd Workshop on the Creation, Curation, Critique and Conditioning of Principles and Guidelines in Visualization},
	year = {2018},
	abstract = {The acquisition process of real world data is usually affected by uncertainty, that can have a huge impact for decision making processes. The uncertainty principle tries to address this problem and gives guidelines to quantify, propagate and communicate the uncertainty of a dataset. Although there exist a variety of work that addresses this principle, the uncertainty principle is not evaluated fully. In this work, we utilize a tumor detection scenario from the medical area and applied the uncertainty principle in its entirety. Based on this, we performed a user evaluation that tries to identify the quality of the uncertainty principle in comparison to a standard visualization approach. We summarize the pros and cons of the uncertainty principles according to different measures. Our study shows, that the uncertainty principle helps users to detect the boundary with more certainty and understand the quality of the underlying image data.},
	author = {Maack, Robin and Hagen, Hans and Gillmann, Christina}
}
@article {858,
	title = {An uncertainty-aware workflow for keyhole surgery planning using hierarchical image semantics},
	journal = {Visual Informatics},
	volume = {2},
	year = {2018},
	abstract = {Keyhole surgeries become increasingly important in clinical daily routine as they help minimizing the damage of a patient{\textquoteright}s healthy tissue. The planning of keyhole surgeries is based on medical imaging and an important factor that influences the surgeries{\textquoteright} success. Due to the image reconstruction process, medical image data contains uncertainty that exacerbates the planning of a keyhole surgery. In this paper we present a visual workflow that helps clinicians to examine and compare different surgery paths as well as visualizing the patients{\textquoteright} affected tissue. The analysis is based on the concept of hierarchical image semantics, that segment the underlying image data with respect to the input images{\textquoteright} uncertainty and the users understanding of tissue composition. Users can define arbitrary surgery paths that they need to investigate further. The defined paths can be queried by a rating function to identify paths that fulfill user-defined properties. The workflow allows a visual inspection of the affected tissues and its substructures. Therefore, the workflow includes a linked view system indicating the three-dimensional location of selected surgery paths as well as how these paths affect the patients tissue. To show the effectiveness of the presented approach, we applied it to the planning of a keyhole surgery of a brain tumor removal and a kneecap surgery.},
	author = {Gillmann, Christina and Maack, Robin and Post, Tobias and Wischgoll, Thomas and Hagen, Hans}
}
