Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
560-03-010 ISMRM Abstract

Uncertainty-Guided Active Learning for Access Route Segmentation and Planning in Transcatheter Aortic Valve Implantation

Primary: Cardiovascular - Vascular and Vessel Wall
Secondary: Analysis Methods - Segmentation and Detection
560-03-010 · Congenital Heart Disease, Valves, and Vessels · Wednesday, 13 May, 1:40 PM–2:35 PM · Digital Posters Row A
Keywords: Cardiovascular magnetic resonance Active learning Vessel diameter quantification Aortic segmentation TAVI planning
Accepted
Enrique Almar-Munoz 1, Mahdi Islam1, Musarrat Tabassum1, Christian Kremser1, Markus Haltmeier2, Agnes Mayr1
1Medical University of Innsbruck, Innsbruck, Austria
2University of Innsbruck, Innsbruck, Austria
Presenting Author: Enrique Almar-Munoz

Synopsis

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References

1. Díez, J. G. (2013). Transcatheter aortic valve implantation (TAVI): the hype and the hope. Texas Heart Institute Journal, 40(3), 298.
2. Al-Najafi, S., Sanchez, F., & Lerakis, S. (2016). The crucial role of cardiac imaging in transcatheter aortic valve replacement (TAVR): pre-and post-procedural assessment. Current treatment options in cardiovascular medicine, 18(12), 70. https://doi.org/10.1007/s11936-016-0497-z [doi]
3. Mahon, C., & Mohiaddin, R. H. (2021). The emerging applications of cardiovascular magnetic resonance imaging in transcatheter aortic valve implantation. Clinical Radiology, 76(1), 73-e21. https://doi.org/10.1016/j.crad.2019.11.011 [doi]
4. Mayr, A., Klug, G., Reinstadler, S. J., Feistritzer, H. J., Reindl, M., Kremser, C., ... & Metzler, B. (2018). Is MRI equivalent to CT in the guidance of TAVR? A pilot study. European Radiology, 28(11), 4625-4634. https://doi.org/10.1007/s00330-018-5386-2 [doi]
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6. Dijkstra, E. W. (2022). A note on two problems in connexion with graphs. In Edsger Wybe Dijkstra: his life, work, and legacy (pp. 287-290). https://doi.org/10.1145/3544585.3544600 [doi]
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8. Sato, Y., Nakajima, S., Atsumi, H., Koller, T., Gerig, G., Yoshida, S., & Kikinis, R. (1997, March). 3D multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. In International Conference on Computer Vision, Virtual Reality, and Robotics in Medicine (pp. 213-222). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/BFb0029240 [doi]
9. Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211. https://doi.org/10.1038/s41592-020-01008-z [doi]
10. Ma, J., Li, F., & Wang, B. (2024). U-mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv preprint arXiv:2401.04722.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/560-03-010