Themen in Magdeburg

The Covid 19 pandemic has once again shown how important it is to communicate medical knowledge and research insights to a broad audience without medical expertise in an understandable way. Recently, narrative visualization, i.e. the combination of storytelling techniques with interactive graphics is used to communicate scientific findings to a broad audience. Many tools have been developed to generate narrative visualizations based on tabular data. However, it is unclear to what extent these tools are also suitable for medical data, which also include 3D surface and volume data, as well as 2D slice images.

Goal: The goal should be to do an extensive literature review. It shall be analyzed which software solutions are available for the creation of data-driven narrative visualizations and on which input data they are based. In addition, it will be analyzed to what extent existing tools are suitable for dealing with medical data and which requirements a medical-oriented tool for the creation of narrative visualizations should fulfill. Optionally, user interfaces can be prototyped and evaluated for such a medical authoring tool.

Type: Bachelor/Master Thesis (Team project (2 FIN students) would also be possible)

Requirements: Good skills in scientific reading and summarizing scientific papers; critical thinking; good transfer skills regarding the derivation of requirements for a medical narrative tool as well as analysis skills whether this is possible with existing tools.

References
[1] Satyanarayan, A., and Heer, J. “Authoring narrative visualizations with ellipsis.” Computer Graphics Forum. Vol. 33. No. 3. 2014.: https://onlinelibrary.wiley.com/doi/full/10.1111/cgf.12392
[2] Conlen, M., and Heer, J. “Idyll: A markup language for authoring and publishing interactive articles on the web.” Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology. 2018.: https://dl.acm.org/doi/abs/10.1145/3242587.3242600
[3] Meuschke, M., et al. “Towards Narrative Medical Visualization.” arXiv preprint arXiv:2108.05462 (2021).: https://arxiv.org/abs/2108.05462

The comprehensible communication of medical research to the broad public plays an important role in many situations, such as education about preventive examinations or vaccinations. Recently, narrative visualization, i.e. the combination of storytelling techniques with interactive graphics is used to communicate scientific findings. A variety of information visualizations such as diagrams and 2D maps have been used to visually communicate scientific findings. However, little research has been done on how comprehensible annotated diagrams such as bubble charts or medical expert diagrams such as Kaplan-Meier plots are for the broad public or how these representations need to be adapted.

Goal: The goal should be to investigate different information visualization techniques regarding their suitability to visually communicate medical information to the broad public. Based on this analysis, guidelines should be derived on how information visualizations need to be adapted to become understandable for people without specific medical background knowledge. The adapted visualizations should be evaluated with participants from the broad public to validate their understandability.

Type: Bachelor/ or Master Thesis (Team project (2 FIN students) would also be possible)

Requirements: Good skills in scientific reading; critical thinking; good skills in graphics programming (exact languages like D3 or OpenGL can be chosen freely) 

References
[1] Morris, T., et al. “Proposals on Kaplan–Meier plots in medical research and a survey of stakeholder views: KMunicate.” BMJ open 9.9 (2019): e030215.: https://bmjopen.bmj.com/content/9/9/e030215.abstract
[2] Drucker, S., et al. “Communicating data to an audience.” In Data-driven storytelling, pp. 211-31. AK Peters/CRC Press, 2018: chrome-extension://oemmndcbldboiebfnladdacbdfmadadm/https://hal.archives-ouvertes.fr/hal-02310515/document
[3] Meuschke, M., et al. “Towards Narrative Medical Visualization.” arXiv preprint arXiv:2108.05462 (2021).: https://arxiv.org/abs/2108.05462

Narrative visualization, i.e. the combination of storytelling techniques with interactive graphics is a powerful option to visually communicate complex data and research findings, such as medical data. A compelling narrative visualization connects data elements or events, often time-related, into a clear and memorable story. Moreover, a story can be told in different ways, e.g., engaging or emotional, depending on the author’s intent and audience. Bach et al. [1] described eighteen narrative design patterns that can be used to tell data stories, see Figure 1. However, little research has been done on which of these patterns are suitable for communicating medical data and issues, and whether specific medical design patterns can be derived.

Goal: The goal should be a comprehensive analysis of medical blogs/YouTube videos and interactive health-related stories in newspapers with respect to narrative patterns detected. Based on this analysis, research questions should be answered, such as whether there is a need for new patterns for medical visualization and whether existing patterns can be made more concrete to better fit medical data.

Type: Bachelor/ or Master Thesis (Team project (2 FIN students) would also be possible)

Requirements: Good skills in scientific reading and summarizing scientific papers; critical and creative thinking.

References
[1] Bach, B., et al. “Narrative design patterns for data-driven storytelling.” Data-driven storytelling. AK Peters/CRC Press, 2018. 107-133.: chrome-extension://oemmndcbldboiebfnladdacbdfmadadm/https://datavis2020.github.io/pdfs/Narrative_Design_Patterns__for_Data_Driven_Storytelling.pdf
[2] Hullman, J., et al., “Visualization rhetoric: Framing effects in narrative visualization,” IEEE Trans Vis Comput Graph, vol. 17, pp. 2231-40, 2011.: https://ieeexplore.ieee.org/abstract/document/6064988
[3] Meuschke, M., et al. “Towards Narrative Medical Visualization.” arXiv preprint arXiv:2108.05462 (2021).: https://arxiv.org/abs/2108.05462

Within the European A-CINCH project, which addresses the loss of the young generation’s interest for nuclear knowledge, virtual experiments are developed.

Tasks:

• Develop a radiochemistry experiement as a VR and web application
• Evaluate its user experience and usability

The project has to be realised in the game engine Unity, thus the following is required:

• Knowledge of basic computer graphics
• Experience with C# and Unity

A tumor board is a discussion among multiple physicians to determine the most appropriate treatment option for a specific cancer patient with a complex disease course. Treatment recommendations are often influenced by previous experience with patients exhibiting similar disease courses. While patient-related image data is displayed during the discussion, clinical patient data, such as the course of tumor days, histological parameters and previously performed treatments, cannot be retrieved at any time and must be repeatedly asked for or memorized. In addition, there is not yet a method to automatically identify and visualize the k most similar patients with their respective therapy trajectories.

Goal: To support physicians during tumor boards in therapy planning for skin cancer patients, the goal of the project is to develop a tumor board visual analytics system that (i) effectively displays longitudinal patient data and (ii) identifies and visualizes characteristics and treatment histories of the most similar previously treated patients.

Team size: 2-3 Master FIN students or Bachelor/Master Thesis
Requirements: Programming experience in D3, Python, or R; critical thinking

References:
[1] Hörbrügger M, Steinhauer N, et al. “Comprehensive Visualization of Longitudinal Patient Data for the Dermatological Oncological Tumor Board.” Proc. of EuroVis 2020. URL: https://diglib.eg.org/handle/10.2312/evs20201067
[2] Prakash S, Unnikrishnan V, Pryss R, Kraft R, Schobel J, Hannemann R, Langguth B, Schlee W, Spiliopoulou M. “Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth Users.” Entropy. 2021; 23(12):1695. https://doi.org/10.3390/e23121695

Immersive virtual reality (VR) simulations are a common possibility to provide surgeons with additional training. One of the main benefits is the high motivation due to a high sense of presence. Nevertheless, the motivation can be increased by adding feedback and gamification aspects. Therefore, we are looking for a student, who will investigate and implement gamification concepts that are appropriate for an already existing medical VR training application.

Your task would be to:

• research existing and similar approaches regarding gamification and acquire some medical background
• develop appropriate gamification concepts
• implement these concepts in an existing VR training application
• conduct a user study to evaluate the developed concepts

The project has to be realised in the game engine Unity, thus the following is required:

• knowledge of basic computer graphics
• experience with C# (and Unity)

AG Visualisierung

Current state:

The chances of success of tumor treatment are highly dependent on the patient’s physical condition. In everyday clinical practice, the patient’s BMI is calculated for this purpose. However, this is a rather inaccurate measure, since the distribution of muscle to fat tissue is a decisive indicator. For a more accurate evaluation, the patient’s CT images must be evaluated. However, this is a time-consuming task.

Scope of the thesis:

This work is intended to address the problem. Currently, data are being acquired in clinical practice and segmented by experts. These are CT data sets in which muscle and fur tissues were segmented in one layer. Your task is to create an automatic segmentation using Deep Learning methods. Subsequently, the segmented regions are to be evaluated with the help of a measure. The explicitly mentioned preprocessing step is to split the given segmentation (symmetry of the body) to provide more data to the network during the learning process. An optional extension would be the automatic selection of the layer in which the evaluation should take place.

We offer:

• interesting clinically relevant research
• support in technical questions and writing of the thesis

We expect:

• good programming skills (Python)
• knowledge of image processing
• experience with Deep Learning and frameworks (Pytorch, Tensorflow, Keras)
• good study achievements

Extension of Shrinkingtubemesh-generation (as illustrated below) with machine learning. The current version was written in matlab and is only suitable for cylinder-like structures, for example vessels. The program should be adjusted to fit a wider variation of shapes.
Subtasks: The task can be solved in two ways: using classical machine learning or with deep learning.

Option 1: Maschine Learning

• including development of a range of suitable startshapes, definition of point cloud features for machine learning, generation of a Testdatabase, usage of Machine Learning to predict a suitable startshape and shrinkingtubemesh-algorithm parameter for a given pointcloud .

Option 2: Deep Learning:

• Generate startshapes (simple, roughly the pointcloud describing meshes) using deep learning (for example using a pointcloud to mesh approach like AtlasNet);
• use these startshapes for the shrinkingtubemesh generation and compare to other mesh generation approaches .

Requirements: Knowledge of Python (Pytorch) and Matlab; Experience in Machine Learning/Deep Learning

We expect high-qualified students interested in this project (hiwi job / student assistant or team projects, bachelor or master thesis). Please send your application!