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Results: 48
Number of items: 48
  • Open Access
    Hampe, N. (2026). Deep learning-based derivation of physiological information from cardiac CT angiography. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    van Harten, L. D., de Jonge, C. S., Struik, F., Stoker, J., & Išgum, I. (2025). Quantitative Analysis of Small Intestinal Motility in 3D Cine‐MRI Using Centerline‐Aware Motion Estimation. Journal of Magnetic Resonance Imaging, 61(4), 1956-1966. https://doi.org/10.1002/jmri.29571
  • Open Access
    van Herten, R. L. M. (2025). Prior-informed deep learning for the analysis and fusion of cardiovascular imaging modalities. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Płotka, S., Szczepański, T., Szenejko, P., Korzeniowski, P., Rodriguez Calvo, J., Khalil, A., Shamshirsaz, A., Brawura-Biskupski-Samaha, R., Išgum, I., Sánchez, C. I., & Sitek, A. (2025). Real-time placental vessel segmentation in fetoscopic laser surgery for Twin-to-Twin Transfusion Syndrome. Medical Image Analysis, 99, Article 103330. https://doi.org/10.1016/j.media.2024.103330
  • Open Access
    Karkalousos, D. (2025). Deep multitask learning for accelerating Magnetic Resonance Imaging. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    van Herten, R. L. M., Lagogiannis, I., Leiner, T., & Išgum, I. (2024). The role of artificial intelligence in coronary CT angiography. Netherlands Heart Journal, 32(11), 417–425. https://doi.org/10.1007/s12471-024-01901-8
  • Open Access
    Galanty, M., Luitse, D., Noteboom, S. H., Croon, P., Vlaar, A. P., Poell, T., Sánchez Gutiérrez, C. I., Blanke, T., & Išgum, I. (2024). Assessing the documentation of publicly available medical image and signal datasets and their impact on bias using the BEAMRAD tool. Scientific Reports, 14, Article 31846. https://doi.org/10.1038/s41598-024-83218-5
  • Open Access
    van Harten, L. D. (2024). Motion analysis in 4D MRI of the small intestine using neural networks. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Williams, M. C., Weir-McCall, J. R., Baldassarre, L. A., De Cecco, C. N., Choi, A. D., Dey, D., Dweck, M. R., Isgum, I., Kolossvary, M., Leipsic, J., Lin, A., Lu, M. T., Motwani, M., Nieman, K., Shaw, L., van Assen, M., & Nicol, E. (2024). Artificial Intelligence and Machine Learning for Cardiovascular Computed Tomography (CCT): A White Paper of the Society of Cardiovascular Computed Tomography (SCCT). Journal of cardiovascular computed tomography, 18(6), 519–532. https://doi.org/10.1016/j.jcct.2024.08.003
  • Open Access
    Alvarez-Florez, L., Sander, J., Bourfiss, M., Tjong, F. V. Y., Velthuis, B. K., & Išgum, I. (2024). Deep Learning for Automatic Strain Quantification in Arrhythmogenic Right Ventricular Cardiomyopathy. In O. Camara, E. Puyol-Antón, M. Sermesant, A. Suinesiaputra, Q. Tao, C. Wang, & A. Young (Eds.), Statistical Atlases and Computational Models of the Heart: Regular and CMRxRecon Challenge Papers: 14th International Workshop, STACOM 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023 : revised selected papers (pp. 25–34). (Lecture Notes in Computer Science; Vol. 14507). Springer. https://doi.org/10.1007/978-3-031-52448-6_3
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