Motivation
Laparoscopic liver surgery is a minimally invasive technique offering several advantages over traditional open surgery, such as smaller incisions, reduced pain, and less scarring. However, it is technically more challenging and demands highly skilled surgeons. To assist during surgery, we are developing a navigation tool that overlays preoperative data onto laparoscopic images. Currently, the required methods are implemented in Python. In the next phase, this software will be integrated into real laparoscopic hardware.

Project Description / Tasks
The main task is to convert different components (e.g., the liver segmentation method) of the existing registration pipeline into the ONNX (Open Neural Network Exchange) format. This enables integration into a real hardware system.

As part of the project:
• You will implement a test environment to compare model performance across formats (ONNX vs. Python).
• You will define suitable performance metrics.
• You will document the performance comparison in a report.

The tasks will be carried out in close cooperation with the supervisor. This HiWi project offers valuable insight into a highly relevant industrial project.

Notes
• Programming knowledge in Python (ONNX experience is a plus but not mandatory)
• The project can be done in English or German
• Motivation and fun, also when contributing your own ideas, are highly desirable
• Key words: model conversion to ONNX format; writing test scripts in Python, reporting results