Table of Contents

1. Introduction

I Medical Visualization Techniques
2. Illustrative Medical Visualization
3. Advanced Vessel Visualization
4. Multimodal Medical Visualization
5. Medical Flow Visualization
6. Medical Animations

II Selected Applications
7. 3D Visualization for Anatomy Education
8. Visual Computing for Radiation Treatment Planning

III Visual Analytics in Healthcare
9. An Introduction to Visual Analytics
10. Visual Analytics in Public Health
11. Visual Analytics in Clinical Medicine

IV Virtual Reality in Medicine
12. Introduction to Virtual Reality
13. Virtual Reality for Medical Education
14. Virtual Reality in Treatment and Rehabilitation

About the Author

Bernhard Preim was born in 1969 in Magdeburg, Germany. He received the diploma in computer science in 1994 (minor in mathematics) and a Ph.D. in 1998 for a thesis on interactive visualization for anatomy education from the Otto-von-Guericke University of Magdeburg. In 1999 he moved to Bremen where he joined the staff of MEVIS and directed the “computer-aided planning in liver surgery” group.
Since Mars 2003 he is full professor for Visualization at the computer science department at the Otto-von-Guericke-University of Magdeburg, heading a research group focussed on medical visualization.
His research interests include vessel visualization, exploration of blood flow, visual analytics in public health, virtual reality in medical education and since recently narrative visualization. He authored “Visualization in Medicine” (Co-author Dirk Bartz, 2007) and “Visual Computing in Medicine” (Co-author: C. Botha, 2013).
Bernhard Preim founded the working group Medical Visualization in the German Society for Computer Science and served as speaker from 2003-2012. He was president of the German Society for Computer- and Robot-Assisted Surgery (www.curac.org). He was Co-Chair and Co-Organizer of the first and second Eurographics Workshop on Visual Computing in Biology and Medicine (VCBM) in 2008 and 2010 and lead the steering committee of that workshop until 2019.
He is the chair of the scientific advisory board of ICCAS (International Competence Center on Computer-Assisted Surgery Leipzig, since 2010). From 2011-2018 he was an associate editor of IEEE Transactions on Medical Imaging and and IEEE Transactions on Visualization and Graphics (2017-2022). Currently he serves in the editorial board of Computers & Graphics (since 2019). He was also regularly a Visiting Professor at the University of Bremen where he closely collaborates with Fraunhofer MEVIS (2003-2012) and was Visiting Professor at TU Vienna (2016). Renata Raidou is Assistant Professor in Medical Visualization and Visual Analytics at the Research Unit of Computer Graphics of the Institute of Visual Computing & Human-Centered Technology, at TU Wien, Austria.
Previously, she was Assistant Professor and Rosalind Franklin Fellow at the Scientific Visualization and Computer Graphics Research Group of the Bernoulli Institute at the University of Groningen, the Netherlands. She did her Post-Doc at the Institute of Visual Computing & Human-Centered Technology, at TU Wien. She received her Ph.D. in Medical Visualization from Eindhoven University of Technology, the Netherlands, in 2017. The topic of her dissertation was “Visual Analytics for Digital Radiotherapy: Towards a Comprehensible Pipeline”, and for the results of her work, she was awarded the Best Ph.D. Award 2018 of the EuroVis Awards Programme. Additionally, she received the Dirk Bartz Prize for Visual Computing in Medicine (1st Place) at Eurographics 2017, and in 2022 she was awarded the EuroVis Young Researcher Award.
Her research focus is on the interface between Visual Analytics, Image Processing, and Machine Learning, with a strong focus on medical applications—in particular, cancer radiotherapy. Her specific domains of expertise are Comparative Visual Analytics and Uncertainty Visualization. She is also interested in visualization topics revolving around biological and medical education. Noeska Smit was born in 1983 in Heenvliet, the Netherlands. She became a licensed radiographer in Rotterdam in 2005. Afterwards, she obtained her master’s in computer science with a minor in biomedical engineering in 2012 at the Delft University of Technology. At the same institute, she defended her PhD thesis on interactive visualization for anatomy education and surgical planning in 2016.
She moved to Bergen, Norway, to join the visualization research group at the Department of Informatics as a tenure-track Associate Professor in medical visualization in 2016. Since 2019, she holds a position as a senior researcher at the Mohn Medical Imaging and Visualization (MMIV) center at the Department of Radiology of the Haukeland University Hospital in Norway. She is part of the leadership team at this interdisciplinary center.
She leads a team researching multimodal medical data visualization. In her interdisciplinary research, she works on novel interactive visualization approaches for improved exploration, analysis, and communication of multimodal medical imaging data. Her work has been awarded with a Dirk Bartz prize for Visual Computing in Medicine in 2019 and the Best Interdisciplinary Presentation Award, awarded at the Computer Graphics International conference in 2020. Kai Lawonn was born in 1985 in Berlin, Germany. He received the diploma in mathematics in 2011 (minor in physics) and a Ph.D. in 2014 for a thesis on illustrative visualization on medical data sets from the Otto-von-Guericke University of Magdeburg. In 2015 he moved to Koblenz where he become a Junior professor at the University of Koblenz-Landau for Medical Visualization.
In 2019 he moved to Jena, Germany, where he is now a full professor for Visualization and Explorative Data Analysis. His work on visualization was awarded with the Best Ph.D. Award 2016 of the Eurographics Awards Programme, the EuroVis Young Researcher Award in 2020, and the Heinz Maier-Leibnitz Prize in 2021.
His research interests include vessel visualization, exploration of blood flow, illustrative visualization, and since recently visualization of machine learning techniques.