Medical Informatics

This group is a subgroup of Life Sciences. Coordinator of this subgroup: Peter A.N. Bosman.
This group is a subgroup of the research group Life Sciences and Health.
Coordinator of this subgroup: Peter A.N. Bosman.
 
 
On the application side, the subgroup of Medical Informatics focuses on applications of computer science and mathematics in medicine and health. Projects typically target demand-driven research questions that, when solved, enable improved (decision) support for medical experts (i.e. clinicians). As such, the Medical Informatics group focuses on fundamental research questions that underlie translational research, connecting models and algorithms from mathematics and computer science to real-world medical practice.

On the fundamental research side, the subgroup of Medical Informatics focuses on the design and application of practically relevant algorithms, mostly in the computational intelligence domain (i.e. metaheuristic optimization (especially (hybrid) evolutionary algorithms) and machine learning). A particular focus is on the design of multi-objective optimization algorithms since real-world optimization problems are often multi-objective, meaning that several objectives need to be optimized simultaneously. This introduces an inherent trade-off between multiple goals (e.g. maximize radiation delivered to tumor cells and minimize radiation delivered to healthy cells).
 
 
A list of projects and (associated) researchers in the Medical Informatics subgroup is provided below.
 
 
Multi-Objective Deformable Image Registration (MODIR) - An Innovative Synergy of Multi-Objective Optimization, Machine Learning, and Biomechanical Modeling for the Registration of Medical Images with Content Mismatch and Large Deformations.
This is a project funded by Dutch organisation for scientific research NWO (Nederlandse organisatie voor Wetenschappelijk Onderzoek) within the domain of Applied and Engineering Sciences and co-funded by industry partners Elekta and Xomnia. The project spports one postdoc (at 1fte for 3 years) and one PhD student at CWI, and one postdoc (at 1fte for 3 years), one PhD student, and one radiation therapist (at 0.5fte for 4 years) at AMC.
 
Improving Childhood Cancer Care when Parents Cannot be There - Reducing Medical Traumatic Stress in Childhood Cancer Patients by Bonding with a Robot Companion.
This is a project funded by Dutch organisation for scientific research NWO (Nederlandse organisatie voor Wetenschappelijk Onderzoek) within the domain of Applied and Engineering Sciences, the Dutch Cancer Society KWF (Koningin Wilhelmina Fonds), and the Top consortia for Knowledge and Innovation Life Sciences & Health TKI-LSH (Topconsortium voor Kennis en Innovatie Life Sciences & Health) and co-funded by industry partners ASolutions BV, Focal Meditech, Brocacef, and Cancer Health Coach. The project supports a postdoc (at 1fte for 2 years) at CWI, 1 PhD student at the Technical University of Delft, and 1 PhD student at AMC (Academic Medical Center). In this project, we develop new technology in AI and robotics and employ these directly in a real-world scenario, to benefit children suffering from childhood cancer. Children treatedfor cancer in a hospital often experience high levels of stress and anxiety. Not only are they faced with a life-threatening disease, they also find themselves in a strange environment, surrounded by doctors, nurses and other medical personnel. Moreover, during various types of treatment, close family may not be in close proximity, e.g., due to exposure risks in case of radiation treatment, or simply because parents cannot guarantee 24/7 presence. This project unites and advances the state-of-the-art in automated human-robot interaction design and machine learning to provide children with a humanoid robot companion that is capable of being there when humans cannot and that can address a child’s distress, pain and fear to minimize medical traumatic stress. This is of key importance, e.g., to ensure successful, uneventful treatment, to lower risks associated with involving relatives and clinicians (e.g., radiation exposure), and to reduce significant adverse psychological reactions (e.g., posttraumatic stress) to the entire experience.
 
 
ICT-based Innovations in the Battle against Cancer - Next-Generation Patient-Tailored Brachytherapy Cancer Treatment Planning.
This is a project funded by the Dutch organisation for scientific research NWO (Nederlandse organisatie voor Wetenschappelijk Onderzoek) within the IPPSI-TA programme (Innovatieve publiek-private samenwerking in ICT (IPPSI) - Technology Area (TA)) and co-funded by industry partners Elekta. The project spports one postdoc (at 1fte for 3 years) and a PhD student at CWI, and two PhD students at AMC (Academic Medical Center). In this project, we tackle demand-driven ICT-related fundamental research challenges that will enable innovations in the important application area of health. We particularly consider the design of brachytherapy (BT) cancer treatment plans. BT is an important type of radiotherapy whereby radiation sources are placed near tumors (using devices such as applicators or interstitial needle implants). ICT plays a crucial role in BT. Software is used to compute, using 3D medical imaging data (e.g., CT- and MRI scans), and ultimately execute, a BT plan to deliver radiation that kills tumor cells. It is, however, impossible to only irradiate tumor cells, putting nearby healthy organs at risk. This makes BT planning hard, requiring medical experts to carefully interact with planning software to find satisfactory plans (see figure for typical example of BT irradiation plan). In this project, we identify several key ICT-related fundamental challenges in optimization and machine learning that will innovate these research areas as well as BT planning and will thereby ultimately improve the patient's quality of life. We design and apply novel MO optimization algorithms and models,  and machine learning algorithms so as to present much better plans to medical experts directly, before fine-tuning is required. We furthermore target exploiting the ever-increasing, ever-cheaper, power of parallel (co-)processing hardware to ensure optimization results are obtained sufficiently fast, while also enabling planning with state-of-the-art radiation models. Finally, we study uncertainties extend our algorithms to make plans that are robust against these uncertainties to realize the best-possible BT cancer treatment plans.
 
3D dose reconstruction for children with long-term follow-up - Toward improved decision making in radiation treatment for children with cancer.
This is a project funded by KiKa (stichting Kinderen Kankervrij), funding one PhD student at CWI, one PhD student and one postdoc (at 0.15fte for 4 years) at AMC (Academic Medical Center). Childhood cancer survivors (CCS) are at significant risk of treatment-related late adverse effects (AEs).  Radiation treatment (RT) is a cornerstone of childhood cancer treatment. It is imperative to understand the underlying relation between radiation dose and AEs to enable the design of the least toxic, yet effective RT plan and to select the best-suited treatment type (e.g., RT with protons versus photons). Currently, late health outcomes cannot always be fully accounted for, because radiation-risk estimates in long-term follow-up studies of CCS are mainly based on rough estimates of average doses to organs, without considering RT-exposed organ volumes. Detailed knowledge of dose and volume effects from 3D dose distributions in relation to multiple AEs is urgently needed. This poses a major challenge because patients with long-term follow-up were treated at a time (<1990) when RT was still planned on 2D simulator films instead of the currently used 3D computed tomography (CT) scans. Therefore, 3D dose distributions are typically not available for these patients. Phantom-based dose-reconstruction methods exist; however, a more individualized technique is preferred. The aim of this project is to develop and validate a novel approach to reconstruct 3D dose distributions of historically-treated patients for whom RT planning CT scans are not available. To this end, state-of-the-art learning and optimization algorithms need to be developed and applied to match each historically-treated patient with recently-treated patients for whom 3D CT scans are available, followed by geometrical and dosimetric reconstructions.
 
 
Optimized targeting for surgery and radiotherapy of breast cancer with a DCIS component.
This is a project funded by KWF that funds two PhD students at NKI (Netherlands Cancer Institute), one of which is co-supervised by senior researchers of CWI and AMC (Academic Medical Center). This project focuses on breast cancer and is aimed at improving imagie-related techniques to facilitate the creation of better radiation treatment plans. The PhD student co-supervised by CWI and AMC focuses mainly on image processing techniques, whereas the PhD student at NKI focuses mainly on image acquisition techniques. Consider the case of breast cancer and having a CT image of a breast that contains a tumor and having another CT image, taken later, where the tumor was surgically removed. Knowing how the anatomy of the breast changed is very important so that follow-up radiation therapy can narrowly target the area that formerly contained the tumor to destroy any remaining cancerous cells without damaging too many healthy cells. Solving this project requires so-called deformable image registration by means one image is transformed to look like the other. The transformation than reveals the required information. The more complex the deformation, the harder currently available techniques are to tune to give acceptable results. In this project, we study different ways of improving on existing approaches, mostly by leveraging already existing approaches, for instance by tuning parameters automatically using a multi-objective approach, or by augmenting existing techniques with external pre-processing steps that can be co-optimized.
 
 
MSc Projects.
Talented students are always welcome to come and do their MSc-thesis work. Students can either propose a topic (will be reviewed before acceptance) or work on topics that contribute to the Medical Informatics subgroup (that are proposed by researchers in the group). Both fundamental algorithmic projects (on evolutionary computation) are possible as well as more application-oriented projects. Feel free to contact Peter A.N. Bosman for more information.
 
 
Researchers at CWI Life Sciences and Health research group
Researchers and Medical Specialists by (project-based) association
  • Cees Witteveen (TU Delft, Algorithmics group, Full Professor)
  • Coen Rasch (AMC, Department of Radiation Oncology, Full Professor)
  • Marcel van Herk (University of Manchester, School of Medical Sciences, Full Professor)
  • Martha Grootenhuis (AMC, Department of Pediatric Psychosocial Care, Full Professor)
  • Arjan Bel (AMC, Department of Radiation Oncology, Head Clinical Physics)
  • Jan-Jakob Sonke (NKI, Department of Radiation Oncology, Group Leader)
  • Tanja Alderliesten (AMC, Department of Radiation Oncology, Senior Researcher)
  • Hans Merks (AMC, Department of Pediatric Oncology, Oncologist)
  • Bradley Pieters (AMC, Department of Radiation Oncology, Radiation Oncologist)
  • Henrike Westerveld (AMC, Department of Radiation Oncology, Radiation Oncologist)
  • Karel Hinnen (AMC, Department of Radiation Oncology, Radiation Oncologist)
  • Niek van Wieringen (AMC, Department of Radiation Oncology, Clinical Physicist)
  • Kees Koedooder (AMC, Department of Radiation Oncology, Clinical Physicist)
  • Brian Balgobind (AMC, Department of Radiation Oncology, Radiation Oncologist)
  • Jan Wiersma (AMC, Department of Radiation Oncology, Clinical Physicist)
  • Koen Hindriks (TU Delft, Interactive Intelligence group, Associate Professor)
  • Dirk Thierens (Utrecht University, Department of Computer Science, Assistant Professor)
  • Cécile Ronckers (AMC, Emma Children's Hospital (DCOG-LATER), Researcher)
  • To be appointed on NWO-TTW MODIR project (Postdoc at AMC)
  • Irma van Dijk (AMC, Department of Radiation Oncology, Postdoc)
  • To be appointed on NWO-TTW MODIR project (PhD Student at AMC)
  • Mike Ligthart (TU Delft, Interactive Intelligence group, PhD student)
  • Kelly van Bindsbergen (AMC, Department of Pediatric Psychosocial Care, PhD student)
  • Marjolein van der Meer (AMC, Department of Radiation Oncology, PhD Student)
  • Stef Maree (AMC, Department of Radiation Oncology, PhD Student)
  • Ziyuan Wang (AMC, Department of Radiation Oncology, PhD Student)
  • Kleopatra Pirpinia (NKI, Department of Radiation Oncology, PhD Student)
  • Krzysztof Sadowski (Utrecht University, Department of Computer Science, PhD Student)

Companies by (project-based) association