Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Suk, Julian"'
Autor:
Suk, Julian, Nannini, Guido, Rygiel, Patryk, Brune, Christoph, Pontone, Gianluca, Redaelli, Alberto, Wolterink, Jelmer M.
Cardiovascular hemodynamic fields provide valuable medical decision markers for coronary artery disease. Computational fluid dynamics (CFD) is the gold standard for accurate, non-invasive evaluation of these quantities in vivo. In this work, we propo
Externí odkaz:
http://arxiv.org/abs/2410.11920
Autor:
Bernárdez, Guillermo, Telyatnikov, Lev, Montagna, Marco, Baccini, Federica, Papillon, Mathilde, Ferriol-Galmés, Miquel, Hajij, Mustafa, Papamarkou, Theodore, Bucarelli, Maria Sofia, Zaghen, Olga, Mathe, Johan, Myers, Audun, Mahan, Scott, Lillemark, Hansen, Vadgama, Sharvaree, Bekkers, Erik, Doster, Tim, Emerson, Tegan, Kvinge, Henry, Agate, Katrina, Ahmed, Nesreen K, Bai, Pengfei, Banf, Michael, Battiloro, Claudio, Beketov, Maxim, Bogdan, Paul, Carrasco, Martin, Cavallo, Andrea, Choi, Yun Young, Dasoulas, George, Elphick, Matouš, Escalona, Giordan, Filipiak, Dominik, Fritze, Halley, Gebhart, Thomas, Gil-Sorribes, Manel, Goomanee, Salvish, Guallar, Victor, Imasheva, Liliya, Irimia, Andrei, Jin, Hongwei, Johnson, Graham, Kanakaris, Nikos, Koloski, Boshko, Kovač, Veljko, Lecha, Manuel, Lee, Minho, Leroy, Pierrick, Long, Theodore, Magai, German, Martinez, Alvaro, Masden, Marissa, Mežnar, Sebastian, Miquel-Oliver, Bertran, Molina, Alexis, Nikitin, Alexander, Nurisso, Marco, Piekenbrock, Matt, Qin, Yu, Rygiel, Patryk, Salatiello, Alessandro, Schattauer, Max, Snopov, Pavel, Suk, Julian, Sánchez, Valentina, Tec, Mauricio, Vaccarino, Francesco, Verhellen, Jonas, Wantiez, Frederic, Weers, Alexander, Zajec, Patrik, Škrlj, Blaž, Miolane, Nina
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM). The challenge focused on the problem
Externí odkaz:
http://arxiv.org/abs/2409.05211
Autor:
Suk, Julian, Alblas, Dieuwertje, Hutten, Barbara A., Wiegman, Albert, Brune, Christoph, van Ooij, Pim, Wolterink, Jelmer M.
Hemodynamic quantities are valuable biomedical risk factors for cardiovascular pathology such as atherosclerosis. Non-invasive, in-vivo measurement of these quantities can only be performed using a select number of modalities that are not widely avai
Externí odkaz:
http://arxiv.org/abs/2408.07110
Many anatomical structures can be described by surface or volume meshes. Machine learning is a promising tool to extract information from these 3D models. However, high-fidelity meshes often contain hundreds of thousands of vertices, which creates un
Externí odkaz:
http://arxiv.org/abs/2403.07536
Blood vessel orientation as visualized in 3D medical images is an important descriptor of its geometry that can be used for centerline extraction and subsequent segmentation and visualization. Arteries appear at many scales and levels of tortuosity,
Externí odkaz:
http://arxiv.org/abs/2311.05400
Autor:
Wiesner, David, Suk, Julian, Dummer, Sven, Nečasová, Tereza, Ulman, Vladimír, Svoboda, David, Wolterink, Jelmer M.
Data-driven cell tracking and segmentation methods in biomedical imaging require diverse and information-rich training data. In cases where the number of training samples is limited, synthetic computer-generated data sets can be used to improve these
Externí odkaz:
http://arxiv.org/abs/2304.08960
Hemodynamic velocity fields in coronary arteries could be the basis of valuable biomarkers for diagnosis, prognosis and treatment planning in cardiovascular disease. Velocity fields are typically obtained from patient-specific 3D artery models via co
Externí odkaz:
http://arxiv.org/abs/2302.08780
Computational fluid dynamics (CFD) is a valuable asset for patient-specific cardiovascular-disease diagnosis and prognosis, but its high computational demands hamper its adoption in practice. Machine-learning methods that estimate blood flow in indiv
Externí odkaz:
http://arxiv.org/abs/2212.05023
Publikováno v:
Medical Image Computing and Computer Assisted Intervention - MICCAI 2022
Methods allowing the synthesis of realistic cell shapes could help generate training data sets to improve cell tracking and segmentation in biomedical images. Deep generative models for cell shape synthesis require a light-weight and flexible represe
Externí odkaz:
http://arxiv.org/abs/2207.06283
Computational fluid dynamics (CFD) is a valuable tool for personalised, non-invasive evaluation of hemodynamics in arteries, but its complexity and time-consuming nature prohibit large-scale use in practice. Recently, the use of deep learning for rap
Externí odkaz:
http://arxiv.org/abs/2109.04797