Visualization, Visual Analytics and Virtual Reality in Medicine

State-of-the-art Techniques and Applications

1st Edition - May 15, 2023
Authors: Bernhard Preim, Renata Raidou, Noeska Smit, Kai Lawonn
Language: English
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 2 2 9 6 2 - 0
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 2 3 1 0 6 - 7

Visualization, Visual Analytics and Virtual Reality in Medicine: State-of-the-art Techniques and Applications describes important techniques and applications that show an understan… Read more

Purchase options

LIMITED OFFER

Save 50% on book bundles

Immediately download your ebook while waiting for your print delivery. No promo code needed.

Institutional subscription on ScienceDirect

Request a sales quote

Visualization, Visual Analytics and Virtual Reality in Medicine: State-of-the-art Techniques and Applications describes important techniques and applications that show an understanding of actual user needs as well as technological possibilities. The book includes user research, for example, task and requirement analysis, visualization design and algorithmic ideas without going into the details of implementation. This reference will be suitable for researchers and students in visualization and visual analytics in medicine and healthcare, medical image analysis scientists and biomedical engineers in general.

Visualization and visual analytics have become prevalent in public health and clinical medicine, medical flow visualization, multimodal medical visualization and virtual reality in medical education and rehabilitation. Relevant applications now include digital pathology, virtual anatomy and computer-assisted radiation treatment planning.

Cover image
Title page
Table of Contents
Copyright
Preface
Chapter 1: Introduction
Abstract
Acknowledgement
References
Part 1: Medical visualization techniques
Introduction
Chapter 2: Illustrative medical visualization
Abstract
2.1. Introduction
2.2. Definition
2.3. Requirements
2.4. Preliminaries
2.5. Illustrative visualization techniques
2.6. Concluding remarks
References
Chapter 3: Advanced vessel visualization
Abstract
3.1. Introduction
3.2. Perception-based vessel visualization
3.3. Integrated visualization of vascular surfaces and embedded flow
3.4. Focus-and-context vessel visualization
3.5. Vessel visualization for diagnosis and treatment planning
3.6. Concluding remarks
References
Chapter 4: Multimodal medical visualization
Abstract
4.1. Introduction
4.2. Medical imaging modalities
4.3. Workflow and requirements
4.4. Visualization techniques
4.5. Rendering and interaction techniques
4.6. Selected applications
4.7. Concluding remarks
References
Chapter 5: Medical flow visualization
Abstract
5.1. Introduction
5.2. Medical background of flow data generation
5.3. Generation of medical flow data
5.4. Task-based visual analysis of medical flow data
5.5. Medical flow analysis systems
5.6. Concluding remarks
References
Chapter 6: Medical animations
Abstract
6.1. Introduction
6.2. Fundamentals
6.3. Medical animations of static data
6.4. Animated volume rendering
6.5. Medical animations of dynamic data
6.6. Applications of animations based on static data
6.7. Interactive animations
6.8. The process of animation generation
6.9. Concluding remarks
References
Part 2: Selected applications
Introduction
Chapter 7: 3D visualization for anatomy education
Abstract
7.1. Introduction
7.2. Educational background
7.3. Datasets
7.4. Visualization techniques
7.5. Knowledge representation and labeling
7.6. Interaction techniques
7.7. Virtual anatomy systems
7.8. 3D web-based anatomy education
7.9. Evaluation of virtual anatomy systems
7.10. Concluding remarks
References
Chapter 8: Visual computing for radiation treatment planning
Abstract
8.1. Introduction
8.2. Background on cancer
8.3. Radiation therapy (RT)
8.4. Definition of target volumes and organs at risk
8.5. Treatment plan design and dose calculation
8.6. Dose plan review and treatment evaluation
8.7. Image-guided adaptive RT
8.8. Concluding remarks
References
Part 3: Visual analytics in healthcare
Introduction
Chapter 9: An introduction to visual analytics
Abstract
9.1. Introduction
9.2. The data–users–tasks design triangle
9.3. Information visualization
9.4. Statistical methods employed in visual analytics
9.5. Dimension reduction
9.6. Clustering
9.7. Subspace clustering
9.8. Association rule mining
9.9. Correlation-based visual analytics
9.10. Interaction
9.11. Challenges in visual analytics for clinical applications
9.12. Concluding remarks
References
Chapter 10: Visual analytics in public health
Abstract
10.1. Introduction
10.2. Public health
10.3. Data for public health
10.4. Commonly used visual analytics techniques
10.5. Analysis and control of epidemics
10.6. Visual analytics for epidemiological research
10.7. Visual analytics of population-based cohort study data
10.8. Evaluation
10.9. Concluding remarks
References
Chapter 11: Visual analytics in clinical medicine
Abstract
11.1. Introduction
11.2. Data in clinical medicine
11.3. Visual analytics of event-type data
11.4. Visualization of single patient data
11.5. Visualization of patient cohort data
11.6. Visual analytics for prediction
11.7. Clinical decision support
11.8. Selected applications
11.9. Concluding remarks
References
Part 4: Virtual Reality in medicine
Introduction
Chapter 12: Introduction to Virtual Reality
Abstract
12.1. Introduction
12.2. Immersion and presence
12.3. VR sickness
12.4. VR hardware
12.5. Avatar design
12.6. Basic interaction techniques
12.7. Locomotion techniques
12.8. Haptics
12.9. Audio feedback
12.10. Applications
12.11. Concluding remarks
References
Chapter 13: Virtual Reality for medical education
Abstract
13.1. Introduction
13.2. Collaborative Virtual Reality
13.3. VR for anatomy education
13.4. VR for surgery training
13.5. Concluding remarks
References
Chapter 14: Virtual Reality in treatment and rehabilitation
Abstract
14.1. Introduction
14.2. Potential of gamification
14.3. VR-based pain management
14.4. VR exposure therapy for treatment of anxieties
14.5. VR for rehabilitation
14.6. Concluding remarks
References
References
References
Index

Bernhard Preim

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).

Affiliations and expertise

Professor of Visualization, Computer Science Department, Otto-von-Guericke-University of Magdeburg, Germany

Renata Raidou

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.

Affiliations and expertise

Assistant Professor in Medical Visualization and Visual Analytics, Research Unit of Computer Graphics of the Institute of Visual Computing & Human-Centered Technology, TU Wien, Austria.

Noeska Smit

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.

Affiliations and expertise

senior researcher, Mohn Medical Imaging and Visualization (MMIV) center, Department of Radiology of the Haukeland University Hospital, Norway

Kai Lawonn

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.

Affiliations and expertise

Assistant Professor for visualization, University of Jena, Germany

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

Visualization, Visual Analytics and Virtual Reality in Medicine

State-of-the-art Techniques and Applications

Purchase options

Save 50% on book bundles

Institutional subscription on ScienceDirect

Bernhard Preim

Renata Raidou

Noeska Smit

Kai Lawonn