Biomedical Imaging Group Rotterdam
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.
We focus on both fundamental and applied research, covering the topics of image analysis, machine learning, image reconstruction, quantitative imaging biomarkers, image-guided interventions, making use of both research data and routine clinical data.
We have a strong outward look: towards other imaging sources, other diagnostic modalities, integrated diagnostics, collaboration with clinical departments. We have strong collaborations with many researchers, clinicians and industry partners.
Sijie and Ruisheng’s (Sijie Liu was a visiting PhD student for two years) paper on ‘segmentation-assisted vessel centerline extraction from cerebral CTA’ has now been officially published in Medical Physics!!
Agora (Erasmus MC intranet) posted an article covering Gennady with the title of ‘Meet Gennady Roshchupkin: Pioneering AI at Erasmus MC’!!
Wietske’s paper on an automatic human embryo volume measurement in first trimester ultrasound from the Rotterdam Periconception Cohort has now been officially published in J Med Internet Res (JMIR)!!
Tumor localization and visualization using magnetic seed tracking and augmented reality
2024 - 2028
Diagnosing liver lesions using multiparametric MRI requires detecting complex, often subtle features - such as washout, fat content, capsular enhancement, and iron deposition - However, these features can be subtle and challenging to detect, often requiring the expertise of experienced radiologists, which limits the scalability of supervised AI approaches. With the rise of vision-language models (VLMs) like GPT-4V and DeepSeek-VL, a key question is whether such models can reliably identify and describe these features across diverse MRI sequences. This project explores the diagnostic potential of VLMs as assistive tools, with a particular focus on benchmarking their ability to see and reason over relevant imaging features in comparison to expert radiologist interpretations and pathology- confirmed diagnoses. The primary goal is to systematically evaluate how well these models extract and describe clinically meaningful features from annotated MRI data, and to what extent their predictions aligns with real-world diagnostic outcomes. Depending on results and interest, the project may also explore fine-tuning existing models to improve domain-specific performance, or - if performance proves reliable - developing a lightweight graphical interface to make these models more accessible and interpretable in a clinical context. This research is part of the Liver Artificial Intelligene - Consortium, a collaborative initiative focused on advancing AI models in liver imaging, bringing together interdisciplinary expertise to unlock new diagnostic possibilities.
Desmoid tumors, also known as aggressive fibromatosis, are rare soft tissue neoplasms that exhibit locally invasive growth but lack metastatic potential. While not typically life-threatening, their infiltrative nature can lead to significant morbidity depending on their location. A large proportion of sporadic desmoid tumors harbor activating mutations in the CTNNB1 gene, which encodes the β-catenin protein. Among these, the S45F mutation is of particular clinical interest as it is associated with increased tumor aggressiveness, higher recurrence rates, and reduced response to conservative therapies.Routine detection of the S45F mutation currently relies on molecular assays such as PCR or sequencing, which, although accurate, are time-consuming, costly, and not always available in low-resource settings. In contrast, hematoxylin and eosin (H&E) stained histopathology slides are inexpensive and routinely available in all diagnostic workflows. Recent advances in computational pathology have shown that deep learning models can infer underlying molecular alterations from subtle morphologic features in H&E slides. This MSc project aims to develop and evaluate a deep learning model that predicts the presence of the S45F mutation in desmoid tumors directly from digitized H&E-stained slides. Such a model could serve as a cost-effective screening tool to complement molecular testing, reduce unnecessary assays, and enhance decision-making in clinical practice. For more information contact: m.starmans@erasmusmc.nl or k.prathaban@erasmusmc.nl.
AI models in medical imaging are usually developed from scratch using disease-specific datasets. While this works for common diseases, it poses a major bottleneck for rare cancers like soft-tissue tumors (STTs), where labelled data is scarce. Unlike humans, who can learn new concepts from just a few examples by drawing on prior experience, current AI systems require large amounts of task-specific data due to how they are trained. This project addresses that limitation by combining meta-learning and automated machine learning (AutoML) to enable knowledge transfer across clinical and reducing reliance on large datasets.
Biomedical Imaging Group Rotterdam is a part of Erasmus MC
