Module CS4331 T

Duration

1 Semester

Turnus of offer

not available anymore

Credit points

4

Course of studies, specific fields and terms:

• Master MES 2020, module part, computer science / electrical engineering
• Master MES 2014, module part, computer science / electrical engineering

Classes and lectures:

• Image Analysis and Visualization Systems in Diagnostics and Therapy (exercise, 1 SWS)
• Image Analysis and Visualization Systems in Diagnostics and Therapy (lecture, 2 SWS)

Workload:

• 20 hours exam preparation
• 55 hours private studies and exercises
• 45 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:
• Data driven segmentation of multispectral image data
• Random Decision Forests for the segmentation of medical image data
• Convolutional Neural Networks and Deep Learning in Medical Image Processing
• Live wire segmentation
• Segmentation with active contour models and deformable models
• Level set segmentation
• Statistical shape models
• Image registration
• Atlas-based segmentation and multi atlas segmentation using non-linear registration
• Visualization techniques in medicine
• Direct volume rendering
• Indirect volume rendering, ray tracing, ray casting
• Haptic 3D interactions in virtual bodies
• Virtual reality techniques in medical applications

Qualification-goals/Competencies:

• The students can classify advanced methods for medical image analysis and visualization, explain them, characterize them on the basis of their properties and select them problem-specifically for a concrete application.
• They are able to explain advanced methods of cluster analysis and classification, especially with Support Vector Machines and Random Decision Forests, and to characterize them based on their properties.
• 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 different non-linear image registration methods and to select and parameterize similarity measures and regularization terms for a specific registration problem.
• They are familiar with methods of multi-atlas segmentation and can explain and exemplarily apply the properties of different label fusion approaches.
• They can distinguish different medical visualization techniques, classify them according to their specific advantages and disadvantages and select and apply them depending on a concrete application problem.
• They can explain different haptic interaction techniques and can classify different systems for VR simulation in medicine.

Grading through:

• Written or oral exam as announced by the examiner

Requires:

• Module part: Medical Image Computing (CS3310 T)

Responsible for this module:

• Siehe Hauptmodul

Teacher:

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

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 2nd edition. Pacific Grove: PWS Publishing 1998
• B. Preim, D. Bartz : Visualization in Medicine Elsevier, 2007

Language:

• offered only in German

Notes:

Last Updated:

08.06.2020