Cancer radiotherapy requires meticulous planning prior to treatment, where the treatment plan is optimized with organ delineations on a pre-treatment scan of the patient. Follow-up analysis in cancer radiotherapy has been inconclusive in the past. In this project, we would like to investigate appropriate visualization solutions (size-based and texture-based) for the support of follow-up studies (i.e., how a patient treatment “evolves” through time).

Note: please mind that there is another topic (link) on follow-up studies support that focuses more on visual analytics (involves machine learning and prediction models).


Design appropriate strategies to support the size-based and texture-based analysis and, subsequent, (illustrative) visualization for follow-up studies in cancer radiotherapy.


  • Interest and knowledge in medical image analysis.
  • Interest and knowledge in visualization (illustrative visualization would be beneficial, but not a pre-requisite).
  • Good programming skills.
  • Creativity and enthusiasm.


To be discussed (depending on the background of the student).




[3] Von Funck, Wolfram, Tino Weinkauf, Holger Theisel, and Hans-Peter Seidel. "Smoke surfaces: An interactive flow visualization technique inspired by real-world flow experiments." IEEE Transactions on Visualization and Computer Graphics 14, no. 6 (2008): 1396-1403.

[4] de Hoon, Niels HLC, Kai Lawonn, Andrei C. Jalba, Elmar Eisemann, and Anna Vilanova. "InkVis: A High-Particle-Count Approach for Visualization of Phase-Contrast Magnetic Resonance Imaging Data." In VCBM, pp. 177-188. 2019.


For more information please contact Renata Raidou.



Master Thesis