Enrolment options

NBE-E4010 - Medical Image Analysis D, Lecture, 2.9.2024-28.11.2024

Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

After this course you should be able to:

  • Implement image smoothing and interpolation techniques
  • Use spatial coordinate systems in medical images
  • Perform landmark-based and intensity-based image registration
  • Select the most appropriate similarity measure for specific image registration problems
  • Implement rigid, affine and nonlinear spatial transformation models
  • Solve segmentation problems using generative models
  • Perform image segmentation using discriminative methods (neural nets)
  • Weigh the advantages and limitations of generative vs. discriminative techniques in medical image analysis

Credits: 5

Schedule: 02.09.2024 - 28.11.2024

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Koen Van Leemput

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

  • valid for whole curriculum period:

    The course aims to give an up-to-date overview of the core techniques used in medical image analysis: image interpolation and smoothing, coordinate systems, image segmentation, and image registration. The focus is enabling students to solve various image analysis problems in practice. Multiple examples of real-life medical image analysis problems will be solved by the students. 

Assessment Methods and Criteria

  • valid for whole curriculum period:

    • Exercise reports that are graded

Workload

  • valid for whole curriculum period:

    • Lectures introducing the various concepts and methods
    • Exercise sessions implementing the methods on real-world examples (Python)
    • Guest lectures and/or excursions (from local industry and hospitals)

DETAILS

Study Material

  • valid for whole curriculum period:

    A dedicated book "Medical Image Analysis" (author: K. Van Leemput) accompanies the lectures and exercises, and presents a self-contained overview and background of the material that will be addressed in the course. 

Substitutes for Courses
Prerequisites

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language: English

    Teaching Period: 2024-2025 Autumn I - II
    2025-2026 Autumn I - II

Rules of Conduct - Students´ Rights and Responsibilities (in force from 1 August 2020)

Aalto University Code of Academic Integrity and Handling Violations Thereof

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