Medical Image Analysis (MIA)
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 segmentation, registration and end with specific machine learning applications in clinical practise. A key to achieving success in the medical image analysis is formal evaluation of methodologies, thus an 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.
MSc Programme in Computer Science
MSc Programme in Physics
The student will at the end of the course have:
- 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 and statistics.
- Rigid & Non-rigid registration + Multi-modal registration.
- Machine learning with medical data
- Applications in lung diseases.
- Applications in neurology.
- Explaining the basics of the underlying physics behind medical image acquisition techniques such as CT, MRI and PET.
- Explaining the role of medical image analysis in relation to detection and prognosis of pathologies and clinical investigations.
- Reading and implementing methods described in the scientific literature in the field of medical imaging.
- Finding and using existing tools within medical image analysis and assessing the quality of the output produced.
- Applying the implemented methods to medical images with the purpose of analysing a specific pathology.
- Analysing, creating and using pipelines of methods for the purpose of analysing medical images in a scientific context.
- Understanding the fundamental challenges in medical image analysis.
- Understanding the representation of images in a computer.
Lectures, exercises, and assignments.
See Absalon when the course is set up.
The students are expected to have a mature and operational
mathematical knowledge. Linear algebra, geometry, basic
mathematical analysis, and basic statistics are mandatory
In the course, we will be using Python as the programming language, and programming skills in Python are highly recommended.
Academic qualifications equivalent to a BSc degree are recommended.
PhD’s can register for MSc-course by following the same procedure as credit-students, see link above.
- 7,5 ECTS
- Type of assessment
- Type of assessment details
- Continuous assessment based on 4-6 written assignments.
The final grade is based on an overall assessment of the assignments.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
Criteria for exam assessment
See Learning Outcome.
Single subject courses (day)
- Course number
- 7,5 ECTS
- Programme level
- Full Degree Master
- Block 1
- No limit
The number of seats may be reduced in the late registration period
- Study Board of Mathematics and Computer Science
- Department of Computer Science
- Faculty of Science
- Melanie Ganz-Benjaminsen (4-4c66737f45696e33707a336970)
Melanie Ganz & Bulat Ibragimov
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Courseinformation of students