Zobrazeno 1 - 10
of 134
pro vyhledávání: '"Mattias P. Heinrich"'
Autor:
Christian Weihsbach, Nora Vogt, Ziad Al-Haj Hemidi, Alexander Bigalke, Lasse Hansen, Julien Oster, Mattias P. Heinrich
Publikováno v:
Sensors, Vol 24, Iss 7, p 2296 (2024)
In cardiac cine imaging, acquiring high-quality data is challenging and time-consuming due to the artifacts generated by the heart’s continuous movement. Volumetric, fully isotropic data acquisition with high temporal resolution is, to date, intrac
Externí odkaz:
https://doaj.org/article/2dd00be9c8ba431eb8d29ea1cc12e8c2
Autor:
Bernhard Kainz, Mattias P. Heinrich, Antonios Makropoulos, Jonas Oppenheimer, Ramin Mandegaran, Shrinivasan Sankar, Christopher Deane, Sven Mischkewitz, Fouad Al-Noor, Andrew C. Rawdin, Andreas Ruttloff, Matthew D. Stevenson, Peter Klein-Weigel, Nicola Curry
Publikováno v:
npj Digital Medicine, Vol 4, Iss 1, Pp 1-18 (2021)
Abstract Deep vein thrombosis (DVT) is a blood clot most commonly found in the leg, which can lead to fatal pulmonary embolism (PE). Compression ultrasound of the legs is the diagnostic gold standard, leading to a definitive diagnosis. However, many
Externí odkaz:
https://doaj.org/article/ab23f06ff7e846daaf92380021bbe382
Publikováno v:
Sensors, Vol 23, Iss 6, p 2876 (2023)
Image registration for temporal ultrasound sequences can be very beneficial for image-guided diagnostics and interventions. Cooperative human–machine systems that enable seamless assistance for both inexperienced and expert users during ultrasound
Externí odkaz:
https://doaj.org/article/1268e3085da34457a364afcb287b7303
Publikováno v:
Sensors, Vol 22, Iss 3, p 1107 (2022)
Deep learning based medical image registration remains very difficult and often fails to improve over its classical counterparts where comprehensive supervision is not available, in particular for large transformations—including rigid alignment. Th
Externí odkaz:
https://doaj.org/article/dc23373ccff04e8d9b48f5aae78b8b27
Autor:
Jeroen Mollink, Michiel Kleinnijenhuis, Anne-Marie van Cappellen van Walsum, Stamatios N. Sotiropoulos, Michiel Cottaar, Christopher Mirfin, Mattias P. Heinrich, Mark Jenkinson, Menuka Pallebage-Gamarallage, Olaf Ansorge, Saad Jbabdi, Karla L. Miller
Publikováno v:
NeuroImage, Vol 157, Iss , Pp 561-574 (2017)
Diffusion MRI is an exquisitely sensitive probe of tissue microstructure, and is currently the only non-invasive measure of the brain's fibre architecture. As this technique becomes more sophisticated and microstructurally informative, there is incre
Externí odkaz:
https://doaj.org/article/8a6ea5607c384484a87127c6d3cfb08d
Autor:
Christian Lucas, André Kemmling, Nassim Bouteldja, Linda F. Aulmann, Amir Madany Mamlouk, Mattias P. Heinrich
Publikováno v:
Frontiers in Neurology, Vol 9 (2018)
Cerebrovascular diseases, in particular ischemic stroke, are one of the leading global causes of death in developed countries. Perfusion CT and/or MRI are ideal imaging modalities for characterizing affected ischemic tissue in the hyper-acute phase.
Externí odkaz:
https://doaj.org/article/01e8d31492f943a09db34b5ce7c5792b
Autor:
Stefan Winzeck, Arsany Hakim, Richard McKinley, José A. A. D. S. R. Pinto, Victor Alves, Carlos Silva, Maxim Pisov, Egor Krivov, Mikhail Belyaev, Miguel Monteiro, Arlindo Oliveira, Youngwon Choi, Myunghee Cho Paik, Yongchan Kwon, Hanbyul Lee, Beom Joon Kim, Joong-Ho Won, Mobarakol Islam, Hongliang Ren, David Robben, Paul Suetens, Enhao Gong, Yilin Niu, Junshen Xu, John M. Pauly, Christian Lucas, Mattias P. Heinrich, Luis C. Rivera, Laura S. Castillo, Laura A. Daza, Andrew L. Beers, Pablo Arbelaezs, Oskar Maier, Ken Chang, James M. Brown, Jayashree Kalpathy-Cramer, Greg Zaharchuk, Roland Wiest, Mauricio Reyes
Publikováno v:
Frontiers in Neurology, Vol 9 (2018)
Performance of models highly depend not only on the used algorithm but also the data set it was applied to. This makes the comparison of newly developed tools to previously published approaches difficult. Either researchers need to implement others'
Externí odkaz:
https://doaj.org/article/c60de6b6cb524c75bed8fa271f3758a6
Autor:
Kumar T. Rajamani, Priya Rani, Hanna Siebert, Rajkumar ElagiriRamalingam, Mattias P. Heinrich
Publikováno v:
Signal, Image and Video Processing. 17:981-989
Deep learning-based image segmentation models rely strongly on capturing sufficient spatial context without requiring complex models that are hard to train with limited labeled data. For COVID-19 infection segmentation on CT images, training data are
Publikováno v:
Medical Imaging 2023: Image Processing.
Publikováno v:
Multimedia Tools and Applications. 81:4535-4547