The research focus “Medical Image Processing, Modelling And Simulation” (MIMAS for short) comprises a cross-section of highly dynamic research topics which, not least due to current technological advances, are becoming increasingly important in medical fields of application.

Medical Image processing: The processing and visualization of medical image data plays a decisive role in diagnostics, documentation, case representation and training. Image data in medicine (photography, 3D image acquisition, X-ray images, computer tomography, magnetic resonance tomography, digital subtraction angiography, ultrasound, optical coherence tomography, positron emission tomography) are being produced in ever larger quantities and of better quality.

Modelling: Modelling in the most general sense describes the simplified representation of reality. In the context of medicine, the models are strongly oriented towards clinical relevance, applicability and available data. Models range from 3D body surface models to blood flow models, which describe the physical interaction between blood and vascular wall.

Simulation: On the one hand, the simulation of processes in the human body enables medical experts and companies in the field of medical technology to evaluate various approaches and methods. An example of this is blood flow simulation, which makes it possible to assess the risk of rupture of an aneurysm. On the other hand, the simulation of processes of medical action can provide a training and education opportunity for physicians in the form of a medical simulator.

These topics are very closely related. Medical image data often form the basis for modelling, while models form the basis for medical image processing and information extraction as well as for the simulation of processes in the human body.

The basic technologies and methods used in these fields also overlap in many ways. GPU (graphics processing unit) based parallel calculations have made the triumphal march of Deep Learning in image processing possible in recent years. GPU-based calculations are also the basis for the simulation of processes in the human body. However, physiological interactions require corresponding models (organs, blood vessels), which are extracted from medical image data using segmentation methods. The registration – calculation of a transformation, which brings two data sets (model, image, volume) into geometric agreement – enables the use of several data sources (image data and model) as well as the transfer of information between different data domains. Information extraction (feature calculation) takes place in different ways in all subject areas. In image processing, image characteristics (texture, brightness, edge information) are extracted, which can be used directly or as input for further methods. For example, geometric properties can be determined from models. The simulation, using the example of blood flow, makes it possible to calculate information such as turbulences or wall shear stresses.

The vision of the research focus is to make these topics available for medical questions on the basis of concrete research hypotheses. The common goal of all work is the further dissemination of individualized and evidence-based medicine. To this end, current methods from research must be further developed with medical experts at an early stage. This is the only way to ensure that, in the medium term, current methods can also be used in clinical practice for the benefit of patients.