Lecture: Image Acquisition and Analysis in Neuroscience

Course

• Lecturer(s):
  • Prof. Dr. Thomas Schultz
  • Dr. Tony Stöcker
• Website:
• Start: 23.10.2013, 10:30 am, LBH III.03a
• Dates: Wed. 10:30 - 12 and Fri. 11 - 12:30
• Course number: MA-INF 2312
• Curriculum: Master , B-IT Master Life Science Informatics
• Requirements:
• Diploma Area:
• Effort: 4.0 SWS
• Max. Participants:
• Exams:
• Follow-up/Side-events:

Exercises

• Tutor(s):
  • Dr. Max Hermann
  • Dr. Michael Ankele
• Start: 08.11.2013
• Dates: Fri. 11 - 12:30, LBH III.03a
• Course number:

Description

This page is outdated. Please find the 2015 class here.

Image-based methods in general and Magnetic Resonance Imaging in particular have become important tools in neuroscience: Imaging the living brain allows us to detect brain regions that engage in specific tasks, to investigate the wiring of the brain, to study and to recognize the effects of aging or disease. Recently, some image-based studies have even made the headlines, suggesting that MR imaging might be used to detect lies, diagnose psychiatric disease, or identify images viewed by the subject in the scanner.

This lecture will make you understand the computer science behind such results. In particular, image acquisition and analysis in neuroscience require the following computational methods:

• Image reconstruction for Magnetic Resonance Imaging
• (Multimodal) image registration
• Building anatomical atlases
• 3D Image Segmentation
• Models for functional MRI (fMRI) and diffusion MRI (dMRI) data
• Statistical Modeling
• Machine Learning Approaches 

The lecture conveys an understanding of all these topics, and is ideally suited to prepare you for a lab course or a master thesis in this field. At the same time, you will learn about some fundamental methods in 3D image processing, statistical modeling, and machine learning, which are useful beyond their applications in neuroscience.

Organization