Student Offers

Scenarios for Narrative Visualization of Medical 3D Datasets

Topic: Raw medical image data is hard to interpret for non-experts. However, to communicate medical topics visualization can help a lot. Therefore, this project strives to find appropriate visualization techniques to make medical image data accessible to the broad public.​ Different visualization techniques should be investigated regarding degrees of interactivity, animation, annotations, etc.

Project Type: There are different possible scenarios, each scenario can be a separate student project. A project can be a Bachelor/Master Thesis, Team project, or Individual project.

Requirements: The project will be done with the Unity Engine (using C# Scripts). Experience with Unity is beneficial but not obligatory. Programming skills in C# or a similar language as well as knowledge of basic computer graphics or visualization are required. Prior knowledge in medical visualization is beneficial but not obligatory.

Scenario 1: Nested Structures

In many cases, the infected tissue lies within healthy tissue. We have to visualize these nested surfaces to not just show the infected tissue but to also provide anatomical context

  • Medical Examples:​
    • Liver Tumor​
    • Brain Lesions​
  • Main Tasks: Implement and Compare Visualization Techniques, e.g.​
    • Transparent Surfaces​
    • Clipping Plane​
    • Cut-a-way-views​
    • Illustrative Techniques​

Scenario 2: Compare Data Sets

Some diseases might not be obvious to non-experts because they don’t know how the healthy anatomical structures look like. Therefore, a comparative visualization of multiple data sets can support their understanding.​

  • Medical Examples:​
    • Aneurysm before and after stent​
  • Main Tasks: Investigate Techniques to Compare Multiple Data Sets
    • Visualization techniques (see Scenario 1) that are suited for comparisons​
    • Techniques to link multiple visualizations together​

Scenario 4: Blood Flow

Visualizing blood flow is important for the explanation of many vascular diseases. However, blood flow can be very complex and it can be tricky to depict different streams inside a vessel without confusing non-experts.

  • Medical Examples:​
    • Flow inside an aneurysm​
    • Flow inside the aorta​
  • Main Task: Implement and Compare Flow Visualization Techniques​
    • Vessel Wall Depictions​
    • Depict Flow as Particles, Lines, …​
    • (Color Choices)​

Scenario 5: Functional Data

Medical visualization includes not only the representation of anatomical structures, but also functional data that is mapped to the anatomical surfaces.​

  • Medical Examples:​
    • Wall shear stress caused by blood flow​
    • (Blood flow velocity)​
  • Main Tasks: Implement and Compare Visualization Techniques for Functional Data​
    • Different Methods for Color-coding​
    • Legends​
    • Glyphs (e.g. like in a weather map)

Adapted InfoVis Graphics to Communicate Medical Data

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) 

[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.:
[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/
[3] Meuschke, M., et al. “Towards Narrative Medical Visualization.” arXiv preprint arXiv:2108.05462 (2021).:

Develop VR/Web Radiochemistry Application for Students

VR Laboratory

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


  • 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

Visual Analytics to Support Tumor Boards in Dermatology

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

[1] Hörbrügger M, Steinhauer N, et al. “Comprehensive Visualization of Longitudinal Patient Data for the Dermatological Oncological Tumor Board.” Proc. of EuroVis 2020. URL:
[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.

Student Project – Gamification concepts for a VR application to train access in skull surgeries

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

Master Thesis: Deep Learning Based Segmentation Task of medical CT-Images based on advanced Preprocessing

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

VR: Build your Aneurysm

Virtual simulations are a common possibility to provide neurosurgeons with additional training for cerebral aneurysm clipping. To provide appropriate training, the data/models (in this case aneurysms) have to be realistic and different cases should be included. This can be done by using patient-specific data. The problem is that the data is not always available and there are rare cases, so the user cannot train with many different models. Therefore, we are searching for a student who will implement an application to build own aneurysms that can be included in an existing clipping simulation.

Your task would be to:

  • research existing and similar approaches and acquire some knowledge about cerebral aneurysms
  • implement an interactive application where the user virtually builds aneurysms

The project should be realized in the game engine Unity, thus the following is required:

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

VR: Simulating the opening of the Dura Mater

Virtual simulations are a common possibility to provide neurosurgeons with additional training for cerebral aneurysm clipping. Before opening the brain, the dura mater (cerebral membrane) has to be removed carefully. This process is often missing in clipping simulations.

Your task would be to:

  • research existing and similar approaches
  • implement an interactive virtual reality application where the user removes the dura mater

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

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

Ausschreibung Studentische Hilfskraft

Wir suchen eine studentische Hilfskraft für die Tätigkeit umfasst eine monatliche Arbeitszeit von mind. 20 Stunden.


  • Gute Kenntnisse in digitaler Bildverarbeitung inbesondere Bildregistrierung
  • Erfahrung im Umgang mit Matlab oder Python, ggf. Erfahrung mit MeVisLab
  • Sehr hohe Motivation
  • Engagement und Teamfähigkeit
  • Strukturiertes und organisiertes Arbeiten

Deine Aufgaben:

  • Entwicklung einer Applikation (z.B. in MeVisLab) zur Registrierung von verschiedenen interventionellen MRT-Bilddatensätzen für die Leber
  • Entwicklung einer Applikation zur Registrierung von histologischen Schnitten zu den individuellen MRT-Datensätzen derselben Leber

Wir bieten Dir:

  • Eine langfristige Anstellungsmöglichkeit mit flexiblen Arbeitszeiten
  • Spannendes Aufgabenfeld mit ausreichender Einarbeitungszeit
  • Bis auf regelmäßige Team-Treffen an der MHH kannst du von überall aus arbeiten

Ausschreibung Studentische Hilfskraft

Wir suchen eine studentische Hilfskraft für die mediznische Visualisierung. Das Anwendungsgebiet sind hier vor allem Aneurysmen des Gehirns. Das sind sackartige, krankhafte Erweiterungen der Blutgefäße welche platzen können und daher möglichst gut untersucht werden müssen. Es gibt verschiedene Aufgaben, z.B. die Visualisierung von künstlich simuliertem Blutfluss oder auch die Darstellung der Aneurysmenwand. Wir haben aber auch die Möglichkeit neue intravaskuläre Bildgebungsmethoden auszuwerten, die höhere Auflösungen als MRT und CT aufweisen. Voraussetzung sind gute Programmierkenntnisse (C++ oder Matlab). Bereits bestehende Erfahrungen mit MeVisLab und VMTK sind hilfreich, aber keine Pflicht.

Anforderungen: Programmiererfahrung in C++ (VTK-Kenntnisse hilfreich) oder MATLAB