Kursussøgning, efter- og videreuddannelse – Københavns Universitet

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Kursussøgning, efter- og videreuddannelse

Medical Image Analysis (MIA)

Practical information
Study year 2016/2017
Time
Block 1
Programme level Full Degree Master
Ph.D.
ECTS 7,5 ECTS
Course responsible
  • Sune Darkner (7-6d6a7b74776e7b496d7237747e376d74)
  • Department of Computer Science
Course number: NDAK10005U

Course content

Medical diagnosis, prognosis and quantification of progression is in general based on biomarkers. These may be blood or urine markers, but currently imaging is taking over as a more indicative biomarker for many purposes.
This course will give an introduction to medical image formation in the different scanning modalities: X-ray, CT, MR, fMRI, PET, US etc. We will continue with the underlying image analysis disciplines of detection, registration, and segmentation, and end with specific applications in clinical practise. A key to achieve success in the medical image analysis is formal evaluation of methodologies, thus a introduction to performance characterisation will also be a central topic.

We will use techniques from image analysis and real world examples from the clinic.

The course aims to  provide sufficient background knowledge for doing master theses (specialer) as well as student projects within medical image analysis.
 

The course is primarily aimed at students from computer science, physics and mathematics with an interest in applications to medical image analysis and related technologies.

 

 

Learning outcome

Knowledge:

After the course the studnt will have knowledge of:

  • Physics of X-ray formation
  • Computed tomography
  • Magnetic Resonance Imaging
  • Functional MRI
  • Positron Emission Tomography
  • Single Photon Emission Tomography
  • Medical statistics
  • Segmentation/Pixel classification
  • Shape modelling
  • Rigid & Non-rigid registration + Multi-modal registration
  • Shape statistics
  • Applications in Lung diseases
  • Application in cardiovascular diseases
  • Applications in joint diseases
  • Applications in neurology

 

Skills:

  • The student will able to explain the basics of the undelying physics behind Medical image acquisition techniques such CT MRI and PET. 
  • The student will able to explain the role of medical image analysis in relation to detection and prognisos of pathologies and clinical investigations.
  • Read and implement methods described in the scientific litterature in the field of medical imaging.
  • Be able to find and use existing tools within medical image analysis and asses the quality of the output produced.
  • Apply implemented methods to medical images with the purpose of analyzing a specific pathology.


Competences.

  • The student will by the end of the course have ability to analyse, create and use pipelines of methods for the purpose of analyzing medical images in a scientific context
  • Understand the fundamental challenges in medical image analysis
  • Understand the representation of images in a computer

Recommended prerequisites

The students are expected to have a mature and operational mathematical knowledge. Linear algebra, geometry, basic mathematical analysis, and basic statistics are mandatory disciplines. Programming skills are not required, but recommended.

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Education

MSc programme in Computer Science

MSc programme in Physics

Studyboard

Study Board of Mathematics and Computer Science

Course type

Single subject courses (day)

Duration

1 block

Schedulegroup

B
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Teaching and learning methods

Lectures, exersises, and assignments

Capacity

No limit

Language

English

Literature

See Absalon when the course is set up.

Workload

Category Hours
Class Instruction 28
Course Preparation 124
Exercises 54
English 206

Exam

Type of assessment

Continuous assessment
Continuous assessment (4-6 written homework assignments).

Marking scale

7-point grading scale

Criteria for exam assessment

See learning outcome.

Censorship form

No external censorship
Several internal examiners

Re-exam

Oral exam (25 minutes without preparation).

Mere information om kurset
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