Computational Surgineering
Overview
This course focuses on exploring the potential of Deep Learning and AI in supporting clinical routine and image-guided interventions. Students will be introduced to real-world challenges in the hospital environment and investigate how AI can assist clinicians in their daily workflow through research-driven prototyping.
Working with experts from the Chair for Computer Aided Medical Procedures (CAMP) at TUM and receiving feedback from clinicians at partner hospitals across Munich, students will explore AI-based approaches tailored to real clinical needs. The focus is on understanding clinical workflows, identifying open research questions, and prototyping proof-of-concept solutions using deep learning methods for medical image analysis and intervention support. The course includes:
- Access to lecture videos about important aspects for building clinical prototypes: OR training, Tools and methods for software development for medical image processing and computer-assisted interventions, Introduction to regulatory aspects of medical software development (for students who missed IGS and PMSD lectures)
- Visit to OR at the clinical partner’s department
- Preliminary presentation
- Project development
- Weekly/Bi-weekly reviews with tutors (help with bug fixing, administrative stuff, connection to experts)
- Prototype demonstration - ideally presented to the doctors - end of the semester
Preliminary meeting slides:
docs.google.com/presentation/d/1QCwjvhATUJ_DzEr_aAG8GKFOGPfLieSZAiZQHKvfPKc/edit
Topic: Computational Surgineering WS25 - Preliminary Meeting
Time: Jul 17, 2025 10:00 AM Amsterdam, Berlin, Rome, Stockholm, Vienna
Join Zoom Meeting
https://tum-conf.zoom-x.de/j/68318802544?pwd=hJY2wSUBEHuWoob05SEA80caIufYwN.1
Recording of the Preliminary meeting:
Passcode: *19!fkHM
Prerequisites and Registration
Experience in programming, in particular Python are needed. Basics of Medical Imaging (e.g., IN2021, IN2022) and Computer Vision are recommended.
- Registration must be done through TUM Matching Platform (please pay attention to the Deadlines)
- Your chances to be assigned to the course increase if you give the course a higher rank in your choices.
- The maximum number of participants: 24.
Objectives
By completing this course, students will be able to:
- Apply selected tools and methods for AI-driven medical image analysis and intervention support
- Gain insight into clinical workflows within a specific specialty or department
- Identify unmet clinical needs and formulate corresponding AI-based research questions
- Define requirements for a clinical AI solution based on real-world observations
- Prototype and evaluate a proof-of-concept solution using retrospective data, simulations, or phantoms
- Communicate their research process and findings to both technical and clinical audiences