Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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Computer Aided Medical Procedures II - IN2022


Lecture: Prof. Nassir Navab

Organization: Dr. Maximilian Baust and Dr. Ulrich Eck

Time & Location


Upon successful completion of the module participants are able to understand the fundamentals, differences, and application areas of advanced methods for image processing, image segmentation and image registration as well as are able to implement them in Python. Moreover, participants are able to understand the fundamentals of machine learning and 3D volume visualization. Furthermore, participants are able to understand complex problems in the area of computer aided diagnosis and interventions as well as to develop solution strategies based on the covered algorithms in the aforementioned areas.


  • Basic knowledge in image computing (not necessarily acquired through the CAMP I lecture)
  • Interest in algorithmic/implementation aspects
  • Specific knowledge in Python is not required but beneficial. However, you should be somehow skilled in programming.


  • Module Spezification: IN2022
  • SWS: 2+2
  • ECTS: 5 Credits
  • Course Language: English
  • Exam: 90 minutes, written, closed book

Course Schedule

Date Time Room Type Topic Lecturer Material
Wed 26.04.2017       Introduction and CPU-based Rendering Dr. Ulrich Eck  slides, [1]
Thu 27.04.2017       Rendering Exercise Dr. Ulrich Eck  exercise  solution
Wed 03.05.2017       Variational Image Segmentation & Denoising Dr. Maximilian Baust  slides, [2-4]
Thu 04.05.2017       Variational Image Segmentation & Denoising Exercise Dr. Maximilian Baust  exercise,  solution
Wed 10.05.2017       Demons Registration Dr. Maximilian Baust  slides, [5-8]
Thu 11.05.2017       Demons Registration Exercise Dr. Maximilian Baust  exercise  solution
Wed 17.05.2017       Ultrasound Imaging Dr. Christoph Hennersperger  slides
Thu 18.05.2017       Hands on Ultrasound Imaging Dr. Christoph Hennersperger -
Wed 24.05.2017       Robotics Dr. Tobias Reichl  slides, supplementary material
Thu 25.05.2017       no lecture (Christi Himmelfahrt)   -
Wed 31.05.2017       Autonomous Robotic US Salvatore Virga  slides
Thu 01.06.2017       Autonomous Robotic US Exercise Salvatore Virga  exercise  solution
Wed 07.06.2017       Introduction to Nuclear Imaging Dr. Benjamin Frisch  slides
Thu 08.06.2017       Coheren Point Drift Repetition Dr. Maximilian Baust  slides, [11-12]
Wed 14.06.2017       Introduction to Machine Learning Shadi Albarqouni  slides, [11-12]
Thu 15.06.2017       no lecture (Fronleichnam)    
Wed 21.06.2017       Digital Pathology µDimensions  slides
Thu 22.06.2017       Digital Pathology µDimensions  slides
Wed 28.06.2017       -- no lecture --    
Thu 29.06.2017       Camera and Tool Calibration Benjamin Busam  slides
Wed 05.07.2017       Tool Tracking Nicola Rieke  slides
Thu 06.07.2017       Tool Tracking Exercise Nicola Rieke  exercise  solution
Wed 12.07.2017       Advanced Visualization Dr. Ulrich Eck  slides
Thu 13.07.2017       Advanced Visualization Exercise Jakob Weiss  exercise  solution
Wed 19.07.2017       questions Dr. Maximilian Baust, Dr. Ulrich Eck  
Tue 25.07.2017 18:30-20:00 Interims Hoersaal 1 (5620.01.101)   Final Exam Dr. Maximilian Baust, Dr. Ulrich Eck  
Thu 07.09.2017 13:00 - 15:00 MI 03.13.040   Exam Inspection Dr. Ulrich Eck  
Fri 20.10.2017 17:00-18:30 Interims Hörsaal 2 (5620.01.102)   Repeat Exam Dr. Ulrich Eck  


  • [1] MATLAB-Python conversion
  • [2] Rudin, L. I., Osher, S., Fatemi, E. (1992). Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena, 60(1-4), 259-268.
  • [3] Chan, T. F., Vese, L. A. (2001). Active contours without edges. IEEE Transactions on image processing, 10(2), 266-277.
  • [4] Chan, T. F., Esedoglu, S., Nikolova, M. (2006). Algorithms for finding global minimizers of image segmentation and denoising models. SIAM journal on applied mathematics, 66(5), 1632-1648.
  • [5] Thirion, J. P. (1998). Image matching as a diffusion process: an analogy with Maxwell's demons. Medical image analysis, 2(3), 243-260.
  • [6] Bro-Nielsen, M., & Gramkow, C. (1996). Fast fluid registration of medical images. In Visualization in Biomedical Computing (pp. 265-276). Springer Berlin Heidelberg.
  • [7] Vercauteren, T., Pennec, X., Perchant, A., & Ayache, N. (2009). Diffeomorphic demons: Efficient non-parametric image registration. NeuroImage?, 45(1), S61-S72.
  • [8] Zikic, D., Baust, M., Kamen, A., & Navab, N. (2011, November). A general preconditioning scheme for difference measures in deformable registration. In Computer Vision (ICCV), 2011 IEEE International Conference on (pp. 49-56). IEEE.
  • [9] Myronenko, A., & Song, X. (2010). Point set registration: Coherent point drift. IEEE transactions on pattern analysis and machine intelligence, 32(12), 2262-2275.
  • [10] Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford university press.
  • [11] Machine Learning with MATLAB (2017)
  • [12] A. Vedaldi, Image Representations, from shallow to deep (2014)

Title: Computer Aided Medical Procedures 2
Professor: Prof. Nassir Navab, Dr. Maximilian Baust, Dr. Ulrich Eck
Type: Lecture
Term: 2017SoSe

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