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Results: 3
Number of items: 3
  • 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
  • Open Access
    Sander, J. (2023). Assessing anatomy and function of the heart using 4D cardiac MRI and deep learning. [Thesis, fully internal, Universiteit van Amsterdam].
  • Sander, J., de Vos, B. D., & Išgum, I. (2021). Unsupervised super-resolution: Creating high-resolution medical images from low-resolution anisotropic examples. In I. Išgum, & B. A. Landman (Eds.), Medical Imaging 2021: Image Processing: 15-19 February 2021, online only, Unitred States (Vol. 1). Article 115960E (Proceedings of SPIE; Vol. 11596), (Progress in Biomedical Optics and Imaging; Vol. 22, No. 2). SPIE. https://doi.org/10.1117/12.2580412
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