Master-Thesis: Soft-Tissue Tension Estimation from Tissue Point Trajectories

10.09.2025, Diplomarbeiten, Bachelor- und Masterarbeiten

In robotic surgery, tension information is critical but very challenging to obtain directly. Visualizing tissue deformation offers a promising way to infer tension indirectly. Recent advances in any-point tracking methods provide an opportunity to bridge the gap between simulation and real-world data, enabling new research directions in surgical robotics.

**Background**
In surgical interventions, surgeons manipulate soft tissue to remove cancerous regions and reconnect healthy structures into a functional state. A key aspect of this process is an intuitive understanding of tissue tension distribution, which helps avoid tissue damage and rupture.
In robotic surgery, such tension information is equally critical but very challenging to obtain directly. Visualizing tissue deformation offers a promising way to infer tension indirectly. Recent advances in any-point tracking methods provide an opportunity to bridge the gap between simulation and real-world data, enabling new research directions in surgical robotics.

**Task**
The aim of this thesis is to leverage tissue-deformation information to infer tissue tension using visual data. The work will start with the development of a simulation-based dataset, followed by the design of methods to estimate tissue tension from tracked deformations, and will conclude with an evaluation on real surgical data.
Subtasks
Within the scope of the thesis, the following points should be addressed:
• Literature review on soft-tissue biomechanics, tension estimation, and visual tracking in surgical robotics
• Construction of a simulation-based dataset capturing tissue deformations under varying conditions
• Development of methods to estimate or approximate tissue tension from deformation information (e.g., using any-point trackers)
• Quantitative evaluation in simulation and transferability assessment on real surgical data
• Discussion of limitations and potential extensions towards clinical applicability

**Literature**
https://arxiv.org/abs/2409.14282
https://deformable-workshop.github.io/icra2025/spotlight/02_04_08_Shinde_JIGGLE.pdf
https://arxiv.org/abs/2309.11655
https://arxiv.org/abs/2309.11656

**Your Profile:**
• Studies in a field relevant to the task description (e.g., computer science)
• Strong background in machine learning, computer vision
• Proficiency in Python and deep learning frameworks such as PyTorch
• Interest in medicine and understanding of surgical robotics

**Application:**
Please send your application with the listed attachments to one of the following addresses: karaoglu@imfusion.com, dennis.schneider@tum.de
Attachments: CV, Transcript of Records, Brief statement of motivation, including relevant background

Kontakt: dennis.schneider@tum.de