Zobrazeno 1 - 10
of 258
pro vyhledávání: '"Delingette Herve"'
Autor:
Wang, Zihao, Yang, Yingyu, Chen, Yuzhou, Yuan, Tingting, Sermesant, Maxime, Delingette, Herve, Wu, Ona
Cross-modality image segmentation aims to segment the target modalities using a method designed in the source modality. Deep generative models can translate the target modality images into the source modality, thus enabling cross-modality segmentatio
Externí odkaz:
http://arxiv.org/abs/2404.01102
Autor:
Wimmer, Wilhelm, Delingette, Hervé
Symmetry detection and morphological classification of anatomical structures play pivotal roles in medical image analysis. The application of kinematic surface fitting, a method for characterizing shapes through parametric stationary velocity fields,
Externí odkaz:
http://arxiv.org/abs/2401.16035
Autor:
Hamzaoui, Dimitri, Montagne, Sarah, Renard-Penna, Raphaële, Ayache, Nicholas, Delingette, Hervé
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2023)
The extraction of consensus segmentations from several binary or probabilistic masks is important to solve various tasks such as the analysis of inter-rater variability or the fusion of several neural network outputs. One of the most widely used meth
Externí odkaz:
http://arxiv.org/abs/2309.08066
Cross-modality data translation has attracted great interest in image computing. Deep generative models (\textit{e.g.}, GANs) show performance improvement in tackling those problems. Nevertheless, as a fundamental challenge in image translation, the
Externí odkaz:
http://arxiv.org/abs/2301.13743
Image registration is an essential but challenging task in medical image computing, especially for echocardiography, where the anatomical structures are relatively noisy compared to other imaging modalities. Traditional (non-learning) registration ap
Externí odkaz:
http://arxiv.org/abs/2211.11687
In privacy-preserving machine learning, it is common that the owner of the learned model does not have any physical access to the data. Instead, only a secured remote access to a data lake is granted to the model owner without any ability to retrieve
Externí odkaz:
http://arxiv.org/abs/2206.03391
Autor:
Blanken, Nathan, Wolterink, Jelmer M., Delingette, Hervé, Brune, Christoph, Versluis, Michel, Lajoinie, Guillaume
Recently, super-resolution ultrasound imaging with ultrasound localization microscopy (ULM) has received much attention. However, ULM relies on low concentrations of microbubbles in the blood vessels, ultimately resulting in long acquisition times. H
Externí odkaz:
http://arxiv.org/abs/2204.04537
Autor:
Chen Zhong, Relan Jatin, Schulze Walther H, Karim Rashed, Sohal Manav, Shetty Anoop, Ma YingLiang, Ayache Nicholas, Sermesant Maxime, Delingette Herve, Bostock Julian, Razavi Reza, Rhode Kawal, Rinaldi Aldo
Publikováno v:
Journal of Cardiovascular Magnetic Resonance, Vol 15, Iss Suppl 1, p P64 (2013)
Externí odkaz:
https://doaj.org/article/2c110d3e0e7e47dca532cc20bdf2b378
This work addresses the problem of non-rigid registration of 3D scans, which is at the core of shape modeling techniques. Firstly, we propose a new kernel based on geodesic distances for the Gaussian Process Morphable Models (GPMMs) framework. The us
Externí odkaz:
http://arxiv.org/abs/2112.11853
Autor:
Wang, Zihao, Delingette, Hervé
Image registration is a crucial task in signal processing, but it often encounters issues with stability and efficiency. Non-learning registration approaches rely on optimizing similarity metrics between fixed and moving images, which can be expensiv
Externí odkaz:
http://arxiv.org/abs/2105.02282