Model-based Medical Image Analysis

The “Model-based Medical Image Analysis” research line of the Biomedical Imaging Group Rotterdam (BIGR) develops novel techniques for quantitative analysis of medical images, with a focus on large-scale image-based studies. An important theme is the application of machine learning techniques in differential diagnosis and prognosis of disease. Such techniques, using statistical prediction models derived from images for which the diagnosis has already been established or for which the future course of the disease is known, are widely applicable and often more robust than conventional image analysis methods. Currently our main application areas are in neuro-, vascular-, and pulmonary image analysis.


  • Quantitative Shape Analysis
  • Pulmonary Image Analysis
  • A transfer learning approach to MRI brain segmentation across scanner protocols
  • Carotid artery segmentation in MR images
  • Representation and transfer learning for medical image analysis
  • Similarity-based transfer learning
  • Aorta and Pulmonary Artery Segmentation in Non-Contrast CT
  • Imaging Dementia: Brain Matters

Associated People


PhD Students

Associated PhD Students

  • Wieying Kuo
  • Raghav Selvan
  • Silas Ørting

Trainees & Visitors

  • Arno van Hilten
  • Camilla Frejlev Bæk
  • Julie Nørgaard Moeslund
  • Signe Skjødt Worsøe-Petersen