- Medical Psychology and Psychotherapy (MPP)
- Health Economics
- Public Health
- Genetic Epidemiology
- Clinical Epidemiology
- Musculoskeletal Science
- Rehabilitation Medicine
- Surgical Research
- Gastroenterology & Hepatology
- Transplantation Medicine
- Periconception and Prenatal Medicine, Obstetrics and Reproduction
- Pediatric Research
- Gynaecology and Gynaecologic Oncology
- Medical Oncology
- Cardiovascular research
- Endocrinology and Neuro-Endocrine Immunology
Through innovative fundamental and applied research Medical Informatics aims at developing and validating advanced techniques for the processing and analysis of large, complex, and heterogeneous medical and biological data sets.
Theme 1: Biomedical Image Processing
Prof. dr. Wiro Niessen
Subtheme 1: Cardiovascular Image Analysis
Prof. dr. Wiro Niessen
State-of-the art imaging techniques have the potential to provide detailed information on the vessel wall, such as plaque composition, elastic wall properties, and even biochemical processes that take place in the plaque. In addition, dynamic and perfusion imaging can provide functional information, e.g. for determining the perfusion or motion of the heart, or to study tumor activity. Owing to the growing complexity and sheer size of cardiovascular data, in combination with the large increase in the number of studies in clinical practice and biomedical research, there is a strong and increasing interest in robust, automated processing tools to aid in the analysis of these data. This research line aims to develop and evaluate novel image processing techniques for visualization, quantification and integrated analysis of multimodal anatomical and functional cardiovascular imaging data.
Subtheme 2: Cellular and Molecular Image Analysis
Dr. Erik Meijering
Advances in imaging technology have revolutionized medicine and biology in the past decades and have opened the door to studying the structure and function of cells and even single molecules.
Biomedical imaging experiments in this area nowadays generate vast amounts of multiparameter spatiotemporal image data containing much more information than can be analyzed by human observers. The goal of our research is to develop advanced image processing and analysis methods to enable efficient, accurate, and reproducible quantification and characterization of cellular and molecular processes. In particular we develop novel methods for image restoration, enhancement, super-resolution, image segmentation, registration, detection, object tracking, and motion analysis. Promising solutions are implemented as user-friendly and publicly available software tools.”
Subtheme 3: Neuro Image Analysis
Dr. Henri Vrooman
Advanced MR brain imaging is widely used in scientific research and clinical practice, as it is a technique that non-invasively provides both anatomical and functional information of the human brain. Nowadays, research is focusing on large imaging population studies to build models of the aging brain.
Using robust, standardized image processing pipelines, several imaging biomarkers, i.e. quantitative information about volume, shape, and functionality of specific brain regions and brain structures, are collected from healthy and diseased subjects. The collected information gives more insight in neurodegenerative diseases and can also be used as reference data on neuro-imaging workstations implemented in the clinic, to give clinicians the possibility to compare patients with memory complaints or cognitive disorders with healthy subjects from the same age and sex. In this way, this research area aims to assist radiologists and referring physicians, yielding a more accurate, better differentiated and earlier diagnosis of brain diseases, such as multiple sclerosis and dementia.
Subtheme 4: Oncological Image Analysis
Dr. Jifke Veenland
One out of three persons develops cancer. Worldwide, much effort is put in developing new treatments and individualizing treatments. For this purpose, markers are being developed to predict and monitor the response of the tumor to the treatment. With MRI it is possible to non-invasively depict the tumor during treatment. In our research we focus on the development and validation of MRI-based image markers for cancer treatments. These markers can be used for tissue characterization, treatment planning, response monitoring and response prediction. Since markers can differ per type of tumor and per treatment, different types of tumors and different types of treatments are studied.
Subtheme 5: Image Guidance in Interventions
Dr. ir. Theo van Walsum
Minimally invasive interventions have distinct advantages for patients. Image guidance is often essential in these interventions, to visualize the target anatomy and the instruments. Current interventional modalities have limitations, which may hamper effective image guidance. E.g. ultrasound imaging often is only 2D, is hard to interpret, and does not always give appropriate contrast between tissues. X-ray imaging is a projection imaging modality, uses harmful ionizing radiation, and requires contrast agents to visualize the vasculature. By incorporating information from pre-operative, diagnostic imaging, is expected to improve image guidance.
This research line aims to develop and evaluate novel image processing techniques for better image guidance, by registering information from e.g. preoperative imaging to the interventional scene. We focus on motion and deformation modeling, and integrating these models in the registration and tracking of target anatomy and instruments during the intervention.
Subtheme 6: Model-based Medical Image Analysis
Dr. Marleen de Bruijne
The “Model-based Medical Image Analysis” research group develops novel techniques for quantitative analysis of medical images, with a focus on statistical learning in large scale image-based studies. An important theme is the application of so-called supervised learning techniques in differential diagnosis and prognosis of disease.
Using statistical models learned from a database of images for which the diagnosis has already been established, or for which the future course of the disease is known from clinical follow-up, such techniques are more widely applicable and often give better results than conventional image analysis methods. Our main applications are in computer-aided diagnosis of neurodegenerative, cardiovascular, and pulmonary disease.
Subtheme 7: Image Registration
Dr. ir. Stefan Klein
Image registration is the task of aligning medical images, such that pixel-by-pixel comparison becomes possible. This is necessary when combining information from different modalities (MRI, CT, Ultrasound), when comparing baseline and follow-up scans, and when comparing the anatomy of different patients. Accurate image registration enables quantitative measurements of tissue atrophy, fully automated motion analysis in 4D (3D+time) datasets, fusion of anatomical and functional imaging data, and it can even be used to create an “average human” based on images of multiple individuals. In our research, we aim to develop fully automatic algorithms for image registration and to use these in various medical imaging applications, such as the analysis of atherosclerotic plaque, the early diagnosis of dementia based on MRI brain scans, and the quantification of tumor response to anti-cancer drugs.
Theme 2: Observational Databases
In the Dutch health care system general practitioners (GPs) play a central role. They practice in the community outside the hospital, referring ambulatory patients to other medical disciplines for outpatient or inpatient care. These other medical disciplines report their findings and actions back to the concerned GPs. The GPs address approximately 90% of the medical problems presented to them. The information systems of the GP, the electronic patient records, are an important source of data due to the unbiased and prospective collection of these data and the detailed insight in patient care. The Integrated Primary Care Information (IPCI) project is a longitudinal collection of electronic patients records from Dutch general practitioners into a central research database. This research area focuses on clinical epidemiologal, pharmaco-epidemiological and pharmaco-economic studies based on this database.
Theme 3: Biosemantics
Dr. Jan A. Kors
The explosion of textual information now available to life scientists has an almost overwhelming effect. It has become entirely impossible to read all relevant literature and interpret all available data in anyone’s discipline.
Predictions for the near future are staggering: in biomedical sciences alone, one new article will be produced every minute in 2007 and this number is likely to increase in the decade to come. This research area focuses on developing innovative tools for sharing the wealth of data sources world-wide. More specific, we develop tools for massive concept mining, enrichment of thesauri and ontologies and meta-analysis of large amounts of distributed resources.
We distinguish four major categories of activities: A: Knowledge creation, discovery and analysis, B: Knowledge validation and annotation, C: (re-)distribution of Knowledge and D: Software and tool development. In close collaboration with many partners, tools are designed, implemented and evaluated to speed up and improve the validation and annotation process, to disambiguate textual variations and to enrich ontologies and thesauri.
The second major subject of the group is the meta-analysis of large numbers of papers. Scientists have been meta-analyzing various literature resources and have come up with new insights through intelligent combination of concepts and their interrelationships. Computational tools that assist the researcher in this combination process have already resulted in new hypotheses. Different technologies will be further developed to allow for massive meta-analysis of hundreds of thousands of database records at a time. The approach is expected to cause a quantum leap in our ability to handle and mine massive amounts of information.