The Biomedical Imaging Group Rotterdam (BIGR) is at the forefront of research in medical image analysis & artificial intelligence (AI). We aim to improve efficiency and quality of healthcare by developing innovative AI methods in medical imaging. BIGR is rooted and embedded in Department of Radiology & Nuclear Medicine of Erasmus MC - University Medical Center Rotterdam, the Netherlands.
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The Biomedical Imaging Group Rotterdam (BIGR) is at the forefront of research in medical image analysis & artificial intelligence (AI). We aim to improve efficiency and quality of healthcare by developing innovative AI methods in medical imaging. BIGR is rooted and embedded in Department of Radiology & Nuclear Medicine of Erasmus MC - University Medical Center Rotterdam, the Netherlands.
Find out more
Health Holland prepared a video on successful public-private partnership projects. It also features the Q-Maestro project, a project where Erasmus MC and Philips have collaborated.
The Department of Radiology & Nuclear Medicine has published its yearly scientific report for 2023
As this month, July, is Sarcoma awareness month, sarcoma research by Erasmus MC is featured on the Dutch sarcoma platform
We develop novel techniques to co-learn from various datatypes using AI to perform integrated diagnostics, focussed on creating imaging-based biomarkers to improve diagnosis and treatment in oncology.
We bring state-of-the-art techniques from computer science to the medical imaging domain, further developing, optimizing and rigorously validating them.
We develop accurate and robust tools for clinicians, such as retinal motion correction and co-localization of different imaging modalities, by using artificial intelligence and image processing techniques on ophthalmic data.
We aim to optimally combine brain imaging, clinical data and artificial intelligence techniques to promote an accurate and early diagnosis, and eventually the right treatment, for patients with neurodegenerative disease.
We are developing trackerless navigation approaches, where the imaging systems used for image guidance are utilized to track the anatomy and instruments, and to align pre-operative 3D models to the interventional images.
Our goals are to improve the understanding of how various omics affect the complex traits and to make use of such insights to improve the diagnosis, prevention and treatment of diseases whenever possible.
We improve the management of prostate cancer patients by developing machine learning-based prognostic models that stratify patients into those that will benefit from additional diagnostic procedures and those that do not.
Our infrastructure can greatly benefit personalized medicine by making pipelines for imaging biomarker extraction available to researchers and clinicians. Additionally, we create a reference database for different imaging biomarkers, which can be used to compare an individual against the general population. This will enable improved re-use of imaging data for diagnostics and prognostics.
We aim to design accelerated quantitative MRI acquisitions and use advanced reconstruction techniques to create images that allow clinical diagnosis to be made.
We develop novel techniques for quantitative analysis of medical images, with a focus on deep learning techniques and on large-scale image-based studies. Currently our main application areas are in neuro-, vascular-, and pulmonary image analysis
We focuse on using artificial intelligence and advanced image analysis techniques to improve diagnosis and prediction of musculoskeletal diseases.
Imaging infrastructure for the COllaboration for New TReatments of Acute Stroke Consortium
2023 - 2028
Euro-BioImaging is a research infrastructure that offers open access to imaging technologies, training and data services in biological and biomedical imaging. Euro-BioImaging consists of imaging facilities, called Nodes, that have opened their doors to all life science researchers.
Health-RI serves as the national coordination point for agreements on the reuse of health data, stimulating cooperation between all parties and supporting researchers.
At the Imaging Office we provide (nearly) ready to use AI pipelines and infrastructure solutions developed at BIGR as a service.
The Departments of Child and Adolescent Psychiatry/Psychology and Radiology & Nuclear Medicine are hiring a full-time Imaging Data Manager. As data manager you will work with two of the world’s largest neuroimaging cohort studies in addition to several novel clinical studies. First, the Department of Child and Adolescent Psychiatry/Psychology has been collecting neuroimaging data in the Generation R Study, a population-based birth cohort, since 2008. Second, the Departments of Epidemiology and Radiology have been collecting neuroimaging data in the Rotterdam Study, a longitudinal cohort of aging, since 2005. Lastly, the Departments of Radiology and Child and Adolescent Psychiatry/Psychology has several ongoing clinical studies where (anonymized) imaging data needs to be retrieved from different centres (both nationally and internationally) and maintained in a database that adheres to the FAIR principles (Findable, Accessible, Interoperable & Reproducible). The data manager is actively working with and contributing to the data management plan of the department(s).
Gadolinium-based contrast agents (GBCAs) are widely used in clinical magnetic resonance imaging (MRI) to identify the presence of brain tumours, lesions in Multiple Sclerosis, and many other tissue abnormalities. Per annum millions of doses of GBCAs are administered in medical imaging centres worldwide. However, there is evidence of contrast agent accumulation in patients, with unknown long-term consequences. Indeed, it is widely recognized that a reduction and even avoidance of contrast agents urgently needed. In a collaborative project the MR Physics group in Erasmus MC and the Medical Imaging Cluster at TU Delft developed new quantitative MR techniques allowing sub-voxel characterization of tissues. Furthermore, preliminary evidence by the MR Physics group suggests that gliomas as well as MS lesions can be characterized based on quantitative MRI without the necessity of gadolinium contrast injection through AI techniques. In this project, we aim to facilitate contrast-free characterization of glioma and MS lesions by combining the best aspects of two revolutionary technologies: quantitative MRI and deep learning AI technology. The project involves a synergistic collaboration between Erasmus MC, TU Delft and GE Healthcare.
Biomedical Imaging Group Rotterdam is a part of Erasmus MC
