Introduction to medical image processing and analysis

The course covers methods and processes for image analysis and research design in medical imaging technology. Key topics include: various steps in image analysis, including the use of artificial intelligence, standards for quality assurance and reporting, as well as principles for designing research projects. The course also addresses acquisition and validation of new software for clinical use and the establishment of governance systems for safe implementation.

A candidate that has completed the course should have the following learning outcomes:

Knowledge:

• Has a broad overview of key steps in image analysis and quality assurance, throughout the entire chain from data collection to preprocessing, analysis, and validation.
• Has good insight into standards and guidelines for quality assurance and reporting of imaging data and image analysis.
• Has knowledge of principles for designing research projects in medical imaging technology, including ethical and legal requirements.
• Understands processes for acquisition and validation of new software for clinical use, as well as the establishment of governance systems for safe implementation

Skills:

• Can assess possibilities and limitations in different types of image processing and analysis.
• Can apply knowledge to plan new research within image processing and analysis.
• Can evaluate regulatory and organizational requirements when introducing new image analysis software and actively contribute to planning and implementing it in clinical practice.

General competence:

• Masters the terminology and understands the functioning of methods for medical imaging and image analysis.
• Can communicate across disciplines about method selection, reproducibility, and implementation of image analysis in clinical and research practice.
• Can analyze issues and results in research involving medical imaging and image analysis.

Campus-based and/or digital lectures and group work with electronic submissions or oral presentations. There will be gatherings (on campus and/or online) over up to eight days during the semester, and students will complete four mandatory assignments. The assignments will be student-active, with exercises using imaging data. All exercises will include a guide for completion. The assignments facilitate thematic integration of theory and practice. All learning resources will be available via an e-learning platform, e.g., articles, links, short videos (animations, lectures, etc.), solutions to tasks, and discussion forums with students and instructors.