The aim of VASCUL-AID is to predict the risk of cardiovascular events and progression of the vascular diseases abdominal aortic aneurysm (AAA) and peripheral arterial disease (PAD). This information will be used improve the patient’s quality of life and care and assisting clinicians to make better-informed decisions involving the patient.
Automatic analysis and outcome prediction of cardiovascular diseases based on medical images is a crucial step in personalized treatment. In recent years, geometric deep learning (GDL) has been shown to be a very efficient approach to modelling patient-specific hemodynamic and biomechanical processes. These techniques can work directly on the 3D data represented in the form of meshes, point clouds or 3D spatial graphs as well as exploiting the symmetries thereof for better efficiency.
In this project, we aim to develop novel geometric deep learning methods integrating various imaging techniques for risk and progression assessment of cardiovascular diseases, in particular AAA and PAD. We will look into deep learning models that utilize geometric and physics-based features to accurately model hemodynamics and biomechanics in (automatically extracted) 3D shapes of vasculatures. Moreover, we aim to study the temporal aspect of the disease progression by taking into account short-term (heart cycle) and long-term (longitudinal data) changes in vasculature shape and its biomechanics. Together with 15 international partner organizations, we aim to leverage geometric deep learning to improve personalized treatment of CVD patients.
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Jump to: 2026 | 2025 | 2024
Geometric deep learning for local growth prediction on abdominal aortic aneurysm surfaces (2026)Artificial intelligence in medicine, 172. Article 103323. Alblas, D., Rygiel, P., Suk, J., Kappe, K. O., Hofman, M., Brune, C., Yeung, K. K. & Wolterink, J. M.https://doi.org/10.1016/j.artmed.2025.103323 Application of Fully Convolutional Neural Networks in the Assessment of Cerebral White Matter Involvement in Primary Sjögren’s Syndrome (2025)Neuroinformatics, 24(1). Article 3 (E-pub ahead of print/First online). Sobański, M., Gajowczyk, M., Rygiel, P., Sobańska, M., Korbecki, A., Litwinowicz, K., Kacała, A., Korbecka, J., Zdanowicz-Ratajczyk, A., Dziadkowiak, E., Sebastian, M., Wiland, P., Trybek, G., Sebastian, A. & Bladowska, J.https://doi.org/10.1007/s12021-025-09762-1Deep vectorised operators for pulsatile hemodynamics estimation in coronary arteries from a steady-state prior (2025)Computer methods and programs in biomedicine, 271. Article 108958. Suk, J., Nannini, G., Rygiel, P., Brune, C., Pontone, G., Redaelli, A. & Wolterink, J. M.https://doi.org/10.1016/j.cmpb.2025.108958Learning hemodynamic scalar fields on coronary artery meshes: A benchmark of geometric deep learning models (2025)Computers in biology and medicine, 195. Article 110477. Nannini, G., Suk, J., Rygiel, P., Saitta, S., Mariani, L., Maranga, R., Baggiano, A., Pontone, G., Wolterink, J. M. & Redaelli, A.https://doi.org/10.1016/j.compbiomed.2025.110477Wall Shear Stress Estimation in Abdominal Aortic Aneurysms: Towards Generalisable Neural Surrogate Models (2025)[Working paper › Preprint]. ArXiv.org. Rygiel, P., Suk, J., Brune, C., Yeung, K. K. & Wolterink, J. M.https://doi.org/10.48550/arXiv.2507.22817Geometric deep learning for local growth prediction on abdominal aortic aneurysm surfaces (2025)[Working paper › Preprint]. ArXiv.org. Alblas, D., Rygiel, P., Suk, J., Kappe, K. O., Hofman, M., Brune, C., Yeung, K. K. & Wolterink, J. M.https://doi.org/10.48550/arXiv.2506.08729Neural Fields for Continuous Periodic Motion Estimation in 4D Cardiovascular Imaging (2025)In Statistical Atlases and Computational Models of the Heart. Workshop, CMRxRecon and MBAS Challenge Papers. - 15th International Workshop, STACOM 2024, Held in Conjunction with MICCAI 2024, Revised Selected Papers: 15th International Workshop, STACOM 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Revised Selected Papers (pp. 378-389) (Lecture Notes in Computer Science; Vol. 15448 LNCS). Springer (E-pub ahead of print/First online). Garzia, S., Rygiel, P., Dummer, S., Cademartiri, F., Celi, S. & Wolterink, J. M.https://doi.org/10.1007/978-3-031-87756-8_37Active Learning for Deep Learning-Based Hemodynamic Parameter Estimation (2025)[Working paper › Preprint]. ArXiv.org. Rygiel, P., Suk, J., Yeung, K. K., Brune, C. & Wolterink, J. M.https://doi.org/10.48550/arXiv.2503.03453Learning Hemodynamic Scalar Fields on Coronary Artery Meshes: A Benchmark of Geometric Deep Learning Models (2025)[Working paper › Preprint]. ArXiv.org. Nannini, G., Suk, J., Rygiel, P., Saitta, S., Mariani, L., Maragna, R., Baggiano, A., Pontone, G., Wolterink, J. M. & Redaelli, A.https://doi.org/10.48550/arXiv.2501.09046Global Control for Local SO(3)-Equivariant Scale-Invariant Vessel Segmentation (2025)In Statistical Atlases and Computational Models of the Heart. Workshop, CMRxRecon and MBAS Challenge Papers: 15th International Workshop, STACOM 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Revised Selected Papers (pp. 269-279) (Lecture Notes in Computer Science; Vol. 15448). Springer. Rygiel, P., Alblas, D., Brune, C., Yeung, K. K. & Wolterink, J. M.https://doi.org/10.1007/978-3-031-87756-8_27 Deep vectorised operators for pulsatile hemodynamics estimation in coronary arteries from a steady-state prior (2024)[Working paper › Preprint]. ArXiv.org. Suk, J., Nannini, G., Rygiel, P., Brune, C., Pontone, G., Redaelli, A. & Wolterink, J. M.https://doi.org/10.48550/arXiv.2410.11920Neural Fields for Continuous Periodic Motion Estimation in 4D Cardiovascular Imaging (2024)[Working paper › Preprint]. ArXiv.org. Garzia, S., Rygiel, P., Dummer, S., Cademartiri, F., Celi, S. & Wolterink, J. M.https://doi.org/10.48550/arXiv.2407.20728AAA-100: A Curated Dataset of 3D Watertight Abdominal Aortic Aneurysm Models (2024)[Dataset Types › Dataset]. Zenodo. Rygiel, P., Alblas, D., Brune, C., Smorenburg, S., Yeung, K. K. & Wolterink, J. M.https://doi.org/10.5281/zenodo.10932956Global Control for Local SO(3)-Equivariant Scale-Invariant Vessel Segmentation (2024)[Working paper › Preprint]. ArXiv.org. Rygiel, P., Alblas, D., Brune, C., Yeung, K. K. & Wolterink, J. M.https://doi.org/10.48550/arXiv.2403.15314
