Zobrazeno 1 - 10
of 236
pro vyhledávání: '"Zachow Stefan"'
Autor:
Rodrigues, Lucas Siqueira, Kosch, Thomas, Nyakatura, John, Zachow, Stefan, Israel, Johann Habakuk
Although digital methods have significantly advanced morphology, practitioners are still challenged to understand and process tomographic specimen data. As automated processing of fossil data remains insufficient, morphologists still engage in intens
Externí odkaz:
http://arxiv.org/abs/2409.17766
Publikováno v:
Current Directions in Biomedical Engineering, Vol 6, Iss 1, Pp 121-7 (2020)
Automatic recognition of surgical phases is an important component for developing an intra-operative context-aware system. Prior work in this area focuses on recognizing short-term tool usage patterns within surgical phases. However, the difference b
Externí odkaz:
https://doaj.org/article/b0865ccc868843adaa871b2cbbf018be
Autor:
Rodrigues, Lucas Siqueira, Schmidt, Timo Torsten, Nyakatura, John, Zachow, Stefan, Israel, Johann Habakuk, Kosch, Thomas
Although Virtual Reality (VR) has undoubtedly improved human interaction with 3D data, users still face difficulties retaining important details of complex digital objects in preparation for physical tasks. To address this issue, we evaluated the pot
Externí odkaz:
http://arxiv.org/abs/2406.14139
Autor:
Lüdke, David, Amiranashvili, Tamaz, Ambellan, Felix, Ezhov, Ivan, Menze, Bjoern, Zachow, Stefan
Statistical shape modeling aims at capturing shape variations of an anatomical structure that occur within a given population. Shape models are employed in many tasks, such as shape reconstruction and image segmentation, but also shape generation and
Externí odkaz:
http://arxiv.org/abs/2209.06861
We present a novel approach for nonlinear statistical shape modeling that is invariant under Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of shapes as elements of Lie groups in a consistent Riemannian setting
Externí odkaz:
http://arxiv.org/abs/2111.06850
Publikováno v:
In Medical Image Analysis May 2024 94
Three-dimensional medical imaging enables detailed understanding of osteoarthritis structural status. However, there remains a vast need for automatic, thus, reader-independent measures that provide reliable assessment of subject-specific clinical ou
Externí odkaz:
http://arxiv.org/abs/2104.01107
Purpose: Segmentation of surgical instruments in endoscopic videos is essential for automated surgical scene understanding and process modeling. However, relying on fully supervised deep learning for this task is challenging because manual annotation
Externí odkaz:
http://arxiv.org/abs/2103.01593
Surgical tool segmentation in endoscopic videos is an important component of computer assisted interventions systems. Recent success of image-based solutions using fully-supervised deep learning approaches can be attributed to the collection of big l
Externí odkaz:
http://arxiv.org/abs/2007.11514
Autor:
Sekuboyina, Anjany, Husseini, Malek E., Bayat, Amirhossein, Löffler, Maximilian, Liebl, Hans, Li, Hongwei, Tetteh, Giles, Kukačka, Jan, Payer, Christian, Štern, Darko, Urschler, Martin, Chen, Maodong, Cheng, Dalong, Lessmann, Nikolas, Hu, Yujin, Wang, Tianfu, Yang, Dong, Xu, Daguang, Ambellan, Felix, Amiranashvili, Tamaz, Ehlke, Moritz, Lamecker, Hans, Lehnert, Sebastian, Lirio, Marilia, de Olaguer, Nicolás Pérez, Ramm, Heiko, Sahu, Manish, Tack, Alexander, Zachow, Stefan, Jiang, Tao, Ma, Xinjun, Angerman, Christoph, Wang, Xin, Brown, Kevin, Kirszenberg, Alexandre, Puybareau, Élodie, Chen, Di, Bai, Yiwei, Rapazzo, Brandon H., Yeah, Timyoas, Zhang, Amber, Xu, Shangliang, Hou, Feng, He, Zhiqiang, Zeng, Chan, Xiangshang, Zheng, Liming, Xu, Netherton, Tucker J., Mumme, Raymond P., Court, Laurence E., Huang, Zixun, He, Chenhang, Wang, Li-Wen, Ling, Sai Ho, Huynh, Lê Duy, Boutry, Nicolas, Jakubicek, Roman, Chmelik, Jiri, Mulay, Supriti, Sivaprakasam, Mohanasankar, Paetzold, Johannes C., Shit, Suprosanna, Ezhov, Ivan, Wiestler, Benedikt, Glocker, Ben, Valentinitsch, Alexander, Rempfler, Markus, Menze, Björn H., Kirschke, Jan S.
Publikováno v:
Medical Image Analysis, Volume 73, October 2021, 102166
Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision-support systems for diagnosis, surgery planning, and p
Externí odkaz:
http://arxiv.org/abs/2001.09193