Novel Geometric Deep Learning Methods for 3D Blood Vessel Analysis
Organization:
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PhD: |
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Supervisors: | chair MIA: Daily supervisor:
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Collaboration: |
Dr. Kak Khee Yeung (Amsterdam UMC) |
Description:
Cardiovascular diseases are the global leading cause of mortality worldwide. Among these diseases are abdominal aortic aneurysms (AAAs), which are characterized by dilations of the vessel wall of the abdominal aorta. The most severe complication of this disorder is rupture of the aorta, which is in many cases fatal. Rupture can be averted using preventive surgeries, however, such interventions are not risk-free. Therefore, both rupture risk and risks associated with post-operative complications should be taken into consideration in AAA patient management.
Medical imaging using MRI, CT and ultrasound plays a key role in the management of patients with AAA. These images are used to extract metrics like the diameter of the aorta, which according to current clinical guidelines is a main indicator of rupture risk. However, such simple metrics are unlikely to tell the full story. Medical images likely contain more information for patient-specific risk prediction. In this project, we aim to bring this information to light by developing novel geometric deep learning techniques for 3D blood vessel analysis
Output:
publications:
Jump to: 2024 | 2023 | 2022 | 2021
Deep-learning-based extraction of circle of Willis topology with anatomical priors (2024)Scientific reports, 14(1), 1-10. Article 31630. Alblas, D., Vos, I. N., Lipplaa, M. M., Brune, C., van der Schaaf, I. C., Velthuis, M. R. E., Velthuis, B. K., Kuijf, H. J., Ruigrok, Y. M. & Wolterink, J. M.https://doi.org/10.1038/s41598-024-80574-0Physics-informed graph neural networks for flow field estimation in carotid arteries (2024)[Working paper › Preprint]. ArXiv.org. Suk, J., Alblas, D., Hutten, B. A., Wiegman, A., Brune, C., van Ooij, P. & Wolterink, J. M.https://doi.org/10.48550/arXiv.2408.07110AAA-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 SIRE: scale-invariant, rotation-equivariant estimation of artery orientations using graph neural networks (2023)[Working paper › Preprint]. ArXiv.org. Alblas, D., Suk, J. M., Brune, C., Yeung, K. K. & Wolterink, J. M.A Joint Data- and Model-Driven Approach Simulating the Behaviour of a Walkalong Glider (2023)[Working paper › Working paper]. Cambridge University Press. Alblas, D., van den Bosch, M., Feigl, Z., Klomp, L. J., Rottschäfer, V., Schwenninger, F., Spek, L., Weedage, L. & Zeijlemaker, S.https://doi.org/10.33774/miir-2023-p957pUncertainty-based quality assurance of carotid artery segmentation (2023)In Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE) (Accepted/In press). Thibeau-Sutre, E., Alblas, D., Buurman, S., Brune, C. & Wolterink, J. M.Uncertainty-based quality assurance of carotid artery wall segmentation in black-blood MRI (2023)[Working paper › Preprint]. ArXiv.org. Thibeau-Sutre, E., Alblas, D., Buurman, S., Brune, C. & Wolterink, J. M.https://doi.org/10.48550/arXiv.2308.09538Implicit Neural Representations for Modeling of Abdominal Aortic Aneurysm Progression (2023)In Functional Imaging and Modeling of the Heart - 12th International Conference, FIMH 2023, Proceedings (pp. 356-365) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13958 LNCS). Springer. Alblas, D., Hofman, M., Brune, C., Yeung, K. K. & Wolterink, J. M.https://doi.org/10.1007/978-3-031-35302-4_37Implicit Neural Representations for Modeling of Abdominal Aortic Aneurysm Progression (2023)[Working paper › Preprint]. ArXiv.org. Alblas, D., Hofman, M., Brune, C., Yeung, K. K. & Wolterink, J. M.https://doi.org/10.48550/arXiv.2303.01069 Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modeling (2022)[Working paper › Preprint]. Alblas, D., Brune, C., Yeung, K. K. & Wolterink, J. M.Deep Learning-Based Carotid Artery Vessel Wall Segmentation in Black-Blood MRI Using Anatomical Priors (2022)In Medical Imaging 2022: Image Processing. Article 120320Y (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 12032). SPIE. Alblas, D., Brune, C. & Wolterink, J. M.https://doi.org/10.1117/12.2611112Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modeling (2022)In Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers: 13th International Workshop, STACOM 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Revised Selected Papers (pp. 79-90). Alblas, D., Brune, C., Yeung, K. K. & Wolterink, J. M.https://doi.org/10.1007/978-3-031-23443-9_8 Pictures:
