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pro vyhledávání: '"Dummer, Sven"'
Autor:
Garzia, Simone, Rygiel, Patryk, Dummer, Sven, Cademartiri, Filippo, Celi, Simona, Wolterink, Jelmer M.
Time-resolved three-dimensional flow MRI (4D flow MRI) provides a unique non-invasive solution to visualize and quantify hemodynamics in blood vessels such as the aortic arch. However, most current analysis methods for arterial 4D flow MRI use static
Externí odkaz:
http://arxiv.org/abs/2407.20728
Autor:
Mazilu, Ioana, Wang, Shunxin, Dummer, Sven, Veldhuis, Raymond, Brune, Christoph, Strisciuglio, Nicola
Though modern microscopes have an autofocusing system to ensure optimal focus, out-of-focus images can still occur when cells within the medium are not all in the same focal plane, affecting the image quality for medical diagnosis and analysis of dis
Externí odkaz:
http://arxiv.org/abs/2307.15461
Diffeomorphic registration frameworks such as Large Deformation Diffeomorphic Metric Mapping (LDDMM) are used in computer graphics and the medical domain for atlas building, statistical latent modeling, and pairwise and groupwise registration. In rec
Externí odkaz:
http://arxiv.org/abs/2305.12854
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
Autor:
Wotte, Yannik, Dummer, Sven, Botteghi, Nicolò, Brune, Christoph, Stramigioli, Stefano, Califano, Federico
It is well known that conservative mechanical systems exhibit local oscillatory behaviours due to their elastic and gravitational potentials, which completely characterise these periodic motions together with the inertial properties of the system. Th
Externí odkaz:
http://arxiv.org/abs/2212.14253
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
Autor:
Wiesner, David, Suk, Julian, Dummer, Sven, Nečasová, Tereza, Ulman, Vladimír, Svoboda, David, Wolterink, Jelmer M.
Publikováno v:
In Medical Image Analysis January 2024 91
Akademický článek
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Shape encoding and shape analysis are valuable tools for comparing shapes and for dimensionality reduction. A specific framework for shape analysis is the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, which is capable of shape mat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::beab66c6d87461c34125fedcd345e82a
Recently, implicit neural representations (INRs) emerged as an effective method for reconstructing shapes. Several of such methods transform templates to target shapes. Current template-based methods lack proper regularization. In this work, we add a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=narcis______::4ed3afd31024309fb7e5f652f88b6cb1
https://research.utwente.nl/en/publications/40502600-6a5c-4163-ade3-3aea23af93c3
https://research.utwente.nl/en/publications/40502600-6a5c-4163-ade3-3aea23af93c3