Module CS4336-KP08

Duration

1 Semester

Turnus of offer

each summer semester

Credit points

8

Course of studies, specific fields and terms:

• Master CLS 2023, compulsory, MML with specialization in Image Processing

Classes and lectures:

• computer graphics (lecture, 2 SWS)
• computer graphics (exercise, 1 SWS)
• Model and AI-based image processing in medicine (exercise, 1 SWS)
• Model and AI-based image processing in medicine (lecture, 2 SWS)

Workload:

• 40 hours exam preparation
• 110 hours private studies and exercises
• 90 hours in-classroom work

Contents of teaching:

• Methods and algorithms for the analysis and visualization of medical images including current research activities in the field of medical image computing. The following methods and algorithms are explained:
• Fundamentals of neural networks in medical image processing
• Convolutional Neural Networks and Deep Learning in Medical Image Processing
• U-Nets for image segmentation
• Autoencoder and Generative Adversarial Networks in Medical Image Processing
• Data augmentation techniques
• Random Decision Forests for the segmentation of medical image data
• Statistical shape models: generation and application for image segmentation
• ROI-based segmentation and cluster analysis for the segmentation of multispectral image data
• Live wire segmentation
• Segmentation with active contour models and deformable models
• Non-linear image registration
• Atlas-based segmentation and multi-atlas segmentation using non-linear registration
• 3D Visualization techniques in medicine
• Geometric transformations in 2D and 3D
• Homogeneous coordinates
• Transformations between Cartesian coordinate systems
• Planar and perspective projections
• Polygonal models
• Illumination models and shading methods
• Texture Mapping
• Culling and clipping
• Hidden line and surface removal
• Raster graphics algorithms
• Ray tracing
• Shadows, reflections and transparency
• Basics of graphics programming with OpenGL and GLSL

Qualification-goals/Competencies:

• Students can classify and explain advanced methods for medical image analysis on the basis of their characteristics. They can select these methods based on a given specific application.
• They are able to explain advanced methods of cluster analysis and classification especially with Convolutional Neural Networks and Random Decision Forests and to characterize them by their properties.
• They can explain the conception of neural network architectures of U-Nets, GANs or auto-encoders in detail. They can explain in detail the conception of neural network architectures of U-Nets, GANs or auto-encoders.
• They know prerequisites, problems and limits as well as augmentation techniques for the use of neural networks in medical image processing.
• They know different approaches to model-based segmentation, can describe the different model assumptions made here and are able to explain the optimization strategies and algorithms used here.
• They are able to assess the properties of various non-linear image registration methods and to select and parametrize similarity measures and regularization terms for a specific registration problem.
• They are familiar with methods of multi-atlas segmentation and can explain and exemplify the properties of different label fusion approaches.
• They can differentiate between different medical visualization techniques, classify them according to their specific advantages and disadvantages, and select and apply them in a meaningful way depending on a specific application problem.
• Students know the basic concepts, algorithms and methods in computer graphics
• They are able to implement principle algorithms
• They are able to explain the learned techniques and to assess their possibilities and limitations

Grading through:

• written exam

Requires:

• Linear Algebra and Discrete Structures 2 (MA1500-KP08, MA1500)
• Linear Algebra and Discrete Structures 1 (MA1000-KP08, MA1000)
• Medical Image Computing (CS3310-KP04)

Responsible for this module:

• Prof. Dr. rer. nat. habil. Heinz Handels

Teacher:

• Institute of Medical Informatics
• Prof. Dr. rer. nat. habil. Heinz Handels
• Dr. rer. nat. Jan Ehrhardt

Literature:

• H. Handels : Medizinische Bildverarbeitung 2. Auflage, Vieweg u. Teubner 2009
• T. Lehmann : Handbuch der Medizinischen Informatik München: Hanser 2005
• M. Sonka, V. Hlavac, R. Boyle : Image Processing, Analysis and Machine Elsevier, 2007
• B. Preim, C. Botha : Visual Computing for Medicine 2nd Edition, Elsevier, 2013
• Foley et. al : Grundlagen der Computergrafik Addison-Wesley, 1994

Language:

• German and English skills required

Notes:

Last Updated:

07.07.2022