Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Giles Tetteh"'
Autor:
Giles Tetteh, Fernando Navarro, Raphael Meier, Johannes Kaesmacher, Johannes C. Paetzold, Jan S. Kirschke, Claus Zimmer, Roland Wiest, Bjoern H. Menze
Publikováno v:
Frontiers in Neurology, Vol 14 (2023)
Collateral circulation results from specialized anastomotic channels which are capable of providing oxygenated blood to regions with compromised blood flow caused by arterial obstruction. The quality of collateral circulation has been established as
Externí odkaz:
https://doaj.org/article/9dcc4d455d704b2ea034ebb7b553cf75
Autor:
Hans Liebl, David Schinz, Anjany Sekuboyina, Luca Malagutti, Maximilian T. Löffler, Amirhossein Bayat, Malek El Husseini, Giles Tetteh, Katharina Grau, Eva Niederreiter, Thomas Baum, Benedikt Wiestler, Bjoern Menze, Rickmer Braren, Claus Zimmer, Jan S. Kirschke
Publikováno v:
Scientific Data, Vol 8, Iss 1, Pp 1-7 (2021)
Measurement(s) vertebra Technology Type(s) computed tomography Factor Type(s) imaging centre • scanner manufacturer Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084
Externí odkaz:
https://doaj.org/article/700b8e5595af4301b434c41fb2464e97
Autor:
Giles Tetteh, Velizar Efremov, Nils D. Forkert, Matthias Schneider, Jan Kirschke, Bruno Weber, Claus Zimmer, Marie Piraud, Björn H. Menze
Publikováno v:
Frontiers in Neuroscience, Vol 14 (2020)
We present DeepVesselNet, an architecture tailored to the challenges faced when extracting vessel trees and networks and corresponding features in 3-D angiographic volumes using deep learning. We discuss the problems of low execution speed and high m
Externí odkaz:
https://doaj.org/article/3362901960b5417d87014b0d3ac514d8
Autor:
Florian Kofler, Christoph Berger, Diana Waldmannstetter, Jana Lipkova, Ivan Ezhov, Giles Tetteh, Jan Kirschke, Claus Zimmer, Benedikt Wiestler, Bjoern H. Menze
Publikováno v:
Frontiers in Neuroscience, Vol 14 (2020)
Despite great advances in brain tumor segmentation and clear clinical need, translation of state-of-the-art computational methods into clinical routine and scientific practice remains a major challenge. Several factors impede successful implementatio
Externí odkaz:
https://doaj.org/article/226942f52d964419a013e1604bc4ed44
Autor:
Song Xue, Andrei Gafita, Chao Dong, Yu Zhao, Giles Tetteh, Bjoern H Menze, Sibylle Ziegler, Wolfgang Weber, Ali Afshar-Oromieh, Axel Rominger, Matthias Eiber, Kuangyu Shi
It is still debating if individualized dose should be applied for the emerging PSMA-targeted radionuclide therapy (RLT). A critical consideration in this debate is the necessity and feasibility of individual estimation of post-therapy dosimetry befor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4a166684eed03da034441ed67eb5b116
https://doi.org/10.21203/rs.3.rs-1588151/v1
https://doi.org/10.21203/rs.3.rs-1588151/v1
Autor:
Song Xue, Andrei Gafita, Chao Dong, Yu Zhao, Giles Tetteh, Bjoern H. Menze, Sibylle Ziegler, Wolfgang Weber, Ali Afshar-Oromieh, Axel Rominger, Matthias Eiber, Kuangyu Shi
Publikováno v:
Xue, Song; Gafita, Andrei; Dong, Chao; Zhao, Yu; Tetteh, Giles; Menze, Bjoern H; Ziegler, Sibylle; Weber, Wolfgang; Afshar-Oromieh, Ali; Rominger, Axel; Eiber, Matthias; Shi, Kuangyu (2022). Application of machine learning to pretherapeutically estimate dosimetry in men with advanced prostate cancer treated with 177Lu-PSMA I&T therapy. European journal of nuclear medicine and molecular imaging, 49(12), pp. 4064-4072. Springer 10.1007/s00259-022-05883-w
Purpose Although treatment planning and individualized dose application for emerging prostate-specific membrane antigen (PSMA)-targeted radioligand therapy (RLT) are generally recommended, it is still difficult to implement in practice at the moment.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3855a88ec43906964b0c0e530fc62dc0
Autor:
Yu Zhao, Andrei Gafita, Axel Rominger, Giles Tetteh, Kuangyu Shi, Fabian Haupt, Matthias Eiber, Bjoern H. Menze, Ali Afshar-Oromieh, Bernd Vollnberg
Publikováno v:
European Journal of Nuclear Medicine and Molecular Imaging. 47:603-613
This study proposes an automated prostate cancer (PC) lesion characterization method based on the deep neural network to determine tumor burden on 68Ga-PSMA-11 PET/CT to potentially facilitate the optimization of PSMA-directed radionuclide therapy. W
Autor:
Giles Tetteh, Wolfgang A. Weber, Matthias Eiber, Fernando Navarro, Marie Bieth, Bjoern H. Menze, Markus Krönke, Elisabeth Günther, Hui Wang, Andrei Gafita
Publikováno v:
Journal of Nuclear Medicine. 60:1277-1283
Our aim was to introduce and validate qPSMA, a semiautomatic software package for whole-body tumor burden assessment in prostate cancer patients using 68Ga-prostate-specific membrane antigen (PSMA) 11 PET/CT. Methods: qPSMA reads hybrid PET/CT images
Autor:
Mohanasankar Sivaprakasam, Timyoas Yeah, Tao Jiang, Xin Wang, Dalong Cheng, Manish Sahu, Maodong Chen, Sebastian Lehnert, Alexander Valentinitsch, Dong Yang, Nicolas Boutry, Shangliang Xu, Johannes C. Paetzold, Alexander Tack, Yujin Hu, Kevin W. Brown, Marilia Lirio, Malek El Husseini, Xu Liming, Darko Štern, Nikolas Lessmann, Suprosanna Shit, Tianfu Wang, Alexandre Kirszenberg, Martin Urschler, Daguang Xu, Feng Hou, Laurence E. Court, Raymond P. Mumme, Maximilian T. Löffler, Sai Ho Ling, Stefan Zachow, Zheng Xiangshang, Markus Rempfler, Yiwei Bai, Elodie Puybareau, Li-Wen Wang, Nicolás Pérez de Olaguer, Moritz Ehlke, Tamaz Amiranashvili, Di Chen, Christoph Angerman, Chan Zeng, Zixun Huang, Jiri Chmelik, Giles Tetteh, Hongwei Li, Jan S. Kirschke, Heiko Ramm, Amirhossein Bayat, Björn H. Menze, Ivan Ezhov, Jan Kukačka, Anjany Sekuboyina, Chenhang He, Ben Glocker, Tucker Netherton, Hans Liebl, Zhiqiang He, Roman Jakubicek, Christian Payer, Felix Ambellan, Supriti Mulay, Lê Duy Huỳnh, Brandon H. Rapazzo, Xinjun Ma, Amber Zhang, Hans Lamecker, Benedikt Wiestler
Publikováno v:
Med. Image Anal. 73:102166 (2021)
Medical Image Analysis, 73
Medical Image Analysis, 73
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e4597e82cc93563d7ce7d43efb18451
https://push-zb.helmholtz-muenchen.de/frontdoor.php?source_opus=62736
https://push-zb.helmholtz-muenchen.de/frontdoor.php?source_opus=62736
Autor:
Marie Piraud, Velizar Efremov, Giles Tetteh, Jan S. Kirschke, Claus Zimmer, Matthias Schneider, Bjoern H. Menze, Bruno Weber, Nils D. Forkert
Publikováno v:
Frontiers in Neuroscience, Vol 14 (2020)
Frontiers in Neuroscience
Frontiers in Neuroscience
We present DeepVesselNet, an architecture tailored to the challenges faced when extracting vessel networks or trees and corresponding features in 3-D angiographic volumes using deep learning. We discuss the problems of low execution speed and high me
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9285085c2c612cec5b940b5a8cbe1a07
https://www.zora.uzh.ch/id/eprint/200234/
https://www.zora.uzh.ch/id/eprint/200234/