Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Kirchhoff, Yannick"'
Autor:
Rokuss, Maximilian, Kirchhoff, Yannick, Roy, Saikat, Kovacs, Balint, Ulrich, Constantin, Wald, Tassilo, Zenk, Maximilian, Denner, Stefan, Isensee, Fabian, Vollmuth, Philipp, Kleesiek, Jens, Maier-Hein, Klaus
Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, m
Externí odkaz:
http://arxiv.org/abs/2409.13416
Autor:
Rokuss, Maximilian, Kovacs, Balint, Kirchhoff, Yannick, Xiao, Shuhan, Ulrich, Constantin, Maier-Hein, Klaus H., Isensee, Fabian
Automated lesion segmentation in PET/CT scans is crucial for improving clinical workflows and advancing cancer diagnostics. However, the task is challenging due to physiological variability, different tracers used in PET imaging, and diverse imaging
Externí odkaz:
http://arxiv.org/abs/2409.09478
Autor:
Kirchhoff, Yannick, Rokuss, Maximilian R., Roy, Saikat, Kovacs, Balint, Ulrich, Constantin, Wald, Tassilo, Zenk, Maximilian, Vollmuth, Philipp, Kleesiek, Jens, Isensee, Fabian, Maier-Hein, Klaus
Accurately segmenting thin tubular structures, such as vessels, nerves, roads or concrete cracks, is a crucial task in computer vision. Standard deep learning-based segmentation loss functions, such as Dice or Cross-Entropy, focus on volumetric overl
Externí odkaz:
http://arxiv.org/abs/2404.03010
Autor:
Yang, Kaiyuan, Musio, Fabio, Ma, Yihui, Juchler, Norman, Paetzold, Johannes C., Al-Maskari, Rami, Höher, Luciano, Li, Hongwei Bran, Hamamci, Ibrahim Ethem, Sekuboyina, Anjany, Shit, Suprosanna, Huang, Houjing, Prabhakar, Chinmay, de la Rosa, Ezequiel, Waldmannstetter, Diana, Kofler, Florian, Navarro, Fernando, Menten, Martin, Ezhov, Ivan, Rueckert, Daniel, Vos, Iris, Ruigrok, Ynte, Velthuis, Birgitta, Kuijf, Hugo, Hämmerli, Julien, Wurster, Catherine, Bijlenga, Philippe, Westphal, Laura, Bisschop, Jeroen, Colombo, Elisa, Baazaoui, Hakim, Makmur, Andrew, Hallinan, James, Wiestler, Bene, Kirschke, Jan S., Wiest, Roland, Montagnon, Emmanuel, Letourneau-Guillon, Laurent, Galdran, Adrian, Galati, Francesco, Falcetta, Daniele, Zuluaga, Maria A., Lin, Chaolong, Zhao, Haoran, Zhang, Zehan, Ra, Sinyoung, Hwang, Jongyun, Park, Hyunjin, Chen, Junqiang, Wodzinski, Marek, Müller, Henning, Shi, Pengcheng, Liu, Wei, Ma, Ting, Yalçin, Cansu, Hamadache, Rachika E., Salvi, Joaquim, Llado, Xavier, Estrada, Uma Maria Lal-Trehan, Abramova, Valeriia, Giancardo, Luca, Oliver, Arnau, Liu, Jialu, Huang, Haibin, Cui, Yue, Lin, Zehang, Liu, Yusheng, Zhu, Shunzhi, Patel, Tatsat R., Tutino, Vincent M., Orouskhani, Maysam, Wang, Huayu, Mossa-Basha, Mahmud, Zhu, Chengcheng, Rokuss, Maximilian R., Kirchhoff, Yannick, Disch, Nico, Holzschuh, Julius, Isensee, Fabian, Maier-Hein, Klaus, Sato, Yuki, Hirsch, Sven, Wegener, Susanne, Menze, Bjoern
The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, character
Externí odkaz:
http://arxiv.org/abs/2312.17670
Autor:
Peretzke, Robin, Maier-Hein, Klaus, Bohn, Jonas, Kirchhoff, Yannick, Roy, Saikat, Oberli-Palma, Sabrina, Becker, Daniela, Lenga, Pavlina, Neher, Peter
Accurately identifying white matter tracts in medical images is essential for various applications, including surgery planning and tract-specific analysis. Supervised machine learning models have reached state-of-the-art solving this task automatical
Externí odkaz:
http://arxiv.org/abs/2305.18905
Autor:
Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor
The ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, whi