Computer Aided Medical Procedures II - IN2022

Organization

Lecture: Prof. Nassir Navab

Organization: Dr. Maximilian Baust

Time & Location

• Wednesdays: 12:30pm - 2:00pm in room MI 03.13.010
• Thursdays: 2:35pm - 4:05pm in room MI 03.13.010

Overview

This lecture teaches students to transfer their knowledge from the field of medical imaging and computer assisted interventions to practical algorithms. During the classes, the students get exposed to particular problems from segmentation, registration, navigation, tracking, and medical augmented reality and are then guided when implementing those algorithms themselves using MATLAB during the exercises. They thus gain deep insight into medical image computing and will be, by the end, able to implement solutions for computer aided medical procedures.

Prerequisites

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

Regulations

• 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 13.04.16	12:30pm - 2:00pm	MI 03.13.010		Introduction	Maximilian Baust	slides, [1]
Thu 14.04.16	2:35pm - 4:05pm	MI 03.13.010		Introduction to MATLAB	Maximilian Baust	slides  introduction.zip  introduction_solution.zip
Wed 20.04.16	12:30pm - 2:00pm	MI 03.13.010		Spatial Domain Image Filtering	Tingying Peng	slides
Thu 21.04.16	2:35pm - 4:05pm	MI 03.13.010		Spatial Domain Image Filtering Exercise	Tingying Peng	exercise  solution
Wed 27.04.16	12:30pm - 2:00pm	MI 03.13.010		Total Variation Image Filtering	Maximilian Baust	slides (updated), [7-8]
Thu 28.04.16	2:35pm - 4:05pm	MI 03.13.010		Total Variation Image Filtering Exercise	Maximilian Baust	exercise  solution  gradients (additional)
Wed 04.05.16	12:30pm - 2:00pm	MI 03.13.010		Frequency Domain Image Filtering	Maximilian Baust	slides, [1]
Thu 05.05.16	no lecture	-	-	-	-	-
Wed 11.05.16	12:30pm - 2:00pm	MI 03.13.010		Frequency Domain Image Filtering Exercise	Maximilian Baust	exercise  solution
Thu 12.05.16	2:35pm - 4:05pm	MI 03.13.010		Basic Segmentation Methods	Kristians Diaz	slides
Wed 18.05.16	12:30pm - 2:00pm	MI 03.13.008		Introduction to Deep Learning	Raphael Prevost
Thu 19.05.16	2:35pm - 4:05pm	MI 03.13.010		Digital Pathology	Hauke Heibel
Wed 25.05.16	12:30pm - 2:00pm	MI 03.13.010		Basic Segmentation Methods Exercise	Kristians Diaz	exercise
Thu 26.05.16	no lecture	-	-	-	-	-
Wed 01.06.16	12:30pm - 2:00pm	MI 03.13.010		Advanced Segmentation Methods	Rüdiger Göbl	slides, [9-11]
Thu 02.06.16	2:35pm - 4:05pm	MI 03.13.010		Advanced Segmentation Methods Exercise	Rüdiger Göbl	exercise,  solution
Wed 08.06.16	12:30pm - 2:00pm	MI 03.13.010		Image Transformations	Ulrich Eck	slides
Thu 09.06.16	2:35pm - 4:05pm	MI 03.13.010		Image Transformations Exercise	Ulrich Eck	exercise,  solution
Wed 15.06.16	12:30pm - 2:00pm	MI 03.13.010		Feature-based Registration	Oliver Zettinig	slides
Thu 16.06.16	2:35pm - 4:05pm	MI 03.13.010		Feature-based Registration	Oliver Zettinig	exercise,  solution
Wed 22.06.16	12:30pm - 2:00pm	MI 03.13.010		Rigid Image Registration	Nicola Rieke	slides
Thu 23.06.16	2:35pm - 4:05pm	MI 03.13.010		Rigid Image Registration Exercise	Nicola Rieke	exercise,  solution
Wed 29.06.16	12:30pm - 2:00pm	MI 03.13.010		Deformable Image Registration	Oliver Zettinig	slides [12]
Thu 30.06.16	2:35pm - 4:05pm	MI 03.13.010		Deformable Image Registration Exercise	Nicola Rieke	exercise,  solution
Wed 06.07.16	12:30pm - 2:00pm	MI 03.13.010		Camera and Tool Calibration	Benjamin Busam	slides
Thu 07.07.16	2:35pm - 4:05pm	MI 03.13.010		Hands-On Camera and Tool Calibration	Benjamin Busam	tasks,  data,  solution
Wed 13.07.16	12:30pm - 2:00pm	MI 03.13.010		Questions	Salvatore Virga
Thu 14.07.16	6:15pm:7:45pm	MI 03.13.010		Exam	Salvatore Virga

Literature

• [1] Cleve Moler. Numerical Computing with MATLAB, Mathworks (2004).
• [2] Tony Lindberg. Scale Space Theory in Computer Vision, Springer (1994).
• [3] Irwin Sobel. An Isotropic 3x3 Image Gradient Operator, personal note (2014).
• [4] Pietro Perona, Jitendra Malik. Scale Space and Edge Detection using Anisotropic Diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence (1990).
• [5] Ery Arias-Castro, David Donoho. Does median filtering truly preserve edges better than linear filtering?, Annals of Statistics (2009).
• [6] Peter Burt, Edward Adelson. The Laplacian Pyramid as a Compact Image Code, IEEE Transactions on Communications (1983).
• [7] Antonin Chambolle, Thomas Pock. A first-order primal-dual algorithm for convex problems with applications to imaging. Journal of Mathematical Imaging and Vision (2011).
• [8] Neal Parikh and Stephen Boyd. Proximal Algorithms, Foudnations and Trends in Optimization (2014).
• [9] Couprie, Camille, et al. Power watershed: A unifying graph-based optimization framework. Pattern Analysis and Machine Intelligence, IEEE Transactions on 33.7 (2011).
• [10] A. K. Sinop and L. Grady. A seeded image segmentation framework unifying graph cuts and random walker which yields a new algorithm. In Proc. of ICCV’07, (2007).
• [11] Grady L.: Random Walks for Image Segmentation, IEEE Pattern Analysis and Machine Intelligence, Vol. 28, No. 11, (2006).
• [12] Aristeidis Sotiras, Christos Davatzikos, and Nikos Paragios. Deformable medical image registration: A survey. Medical Imaging, IEEE Transactions on, 32(7):1153–1190, 2013.